techlist.io
KR EN

prompt-engineering

13 posts

toss

Will developers be replaced by AI? (opens in new tab)

The current AI hype cycle is a significant economic bubble where massive infrastructure investments of $560 billion far outweigh the modest $35 billion in generated revenue. However, drawing parallels to the 1995 dot-com era, the author argues that while short-term expectations are overblown, the long-term transformation of the developer role is inevitable. The conclusion is that developers won't be replaced but will instead evolve into "Code Creative Directors" who manage AI through the lens of technical abstraction and delegation. ### The Economic Bubble and Amara’s Law * The industry is experiencing a 16:1 imbalance between AI investment and revenue, with 95% of generative AI implementations reportedly failing to deliver clear efficiency improvements. * Amara’s Law suggests that we are overestimating AI's short-term impact while potentially underestimating its long-term necessity. * Much of the current "AI-driven" job market contraction is actually a result of companies cutting personnel costs to fund expensive GPU infrastructure and AI research. ### Jevons Paradox and the Evolution of Roles * Jevons Paradox indicates that as the "cost" of producing code drops due to AI efficiency, the total demand for software and the complexity of systems will paradoxically increase. * The developer’s identity is shifting from "code producer" to "system architect," focusing on agent orchestration, result verification, and high-level design. * AI functions as a "power tool" similar to game engines, allowing small teams to achieve professional-grade output while amplifying the capabilities of senior engineers. ### Delegation as a Form of Abstraction * Delegating a task to AI is an act of "work abstraction," which involves choosing which low-level details a developer can afford to ignore. * The technical boundary of what is "hard to delegate" is constantly shifting; for example, a complex RAG (Retrieval-Augmented Generation) pipeline built for GPT-4 might become obsolete with the release of a more capable model like GPT-5. * The focus for developers must shift from "what is easy to delegate" to "what *should* be delegated," distinguishing between routine boilerplate and critical human judgment. ### The Risks of Premature Abstraction * Abstraction does not eliminate complexity; it simply moves it into the future. If the underlying assumptions of an AI-generated system change, the abstraction "leaks" or breaks. * Sudden shifts in scaling (traffic surges), regulation (GDPR updates), or security (zero-day vulnerabilities) expose the limitations of AI-delegated work, requiring senior intervention. * Poorly managed AI delegation can lead to "abstraction debt," where the cost of fixing a broken AI-generated system exceeds the cost of having written it manually from the start. To thrive in this environment, developers should embrace AI not as a replacement, but as a layer of abstraction. Success requires mastering the ability to define clear boundaries for AI—delegating routine CRUD operations and boilerplate while retaining human control over architecture, security, and complex business logic.

prompt-engineeringaillmgen-ai+3
line

Building an Enterprise LLM Service 1 (opens in new tab)

LY Corporation’s engineering team developed an AI assistant for their private cloud platform, Flava, by prioritizing "context engineering" over traditional prompt engineering. To manage a complex environment of 260 APIs and hundreds of technical documents, they implemented a strategy of progressive disclosure to ensure the LLM receives only the most relevant information for any given query. This approach allows the assistant to move beyond simple RAG-based document summarization to perform active diagnostics and resource management based on real-time API data. ### Performance Limitations of Long Contexts * Research indicates that LLM performance can drop by 13.9% to 85% as context length increases, even if the model technically supports a large token window. * The phenomenon of "context rot" occurs when low-quality or irrelevant information is mixed into the input, causing the model to generate confident but incorrect answers. * Because LLMs are stateless, maintaining conversation history and processing dense JSON responses from multiple APIs quickly exhausts context windows and degrades reasoning quality. ### Progressive Disclosure and Tool Selection * The system avoids loading all 260+ API definitions at once; instead, it analyzes the user's intent to select only the necessary tools, such as loading only Redis-related APIs when a user asks about a cluster. * Specific product usage hints, such as the distinction between private and CDN settings for Object Storage, are injected only when those specific services are invoked. * This phased approach significantly reduces token consumption and prevents the model from being overwhelmed by irrelevant technical specifications. ### Response Guidelines and the "Mock Tool Message" Strategy * The team distinguished between "System Prompts" (global rules) and "Response Guidelines" (situational instructions), such as directing users to a console UI before suggesting CLI commands. * Injecting specific guidelines into the system prompt often caused "instruction conflict," where the LLM might hallucinate information to satisfy a guideline while ignoring core requirements like using search tools. * To resolve these conflicts, the team utilized "ToolMessages" to inject guidelines; by formatting instructions as if they were results from a tool execution, the LLM treats the information as factual context rather than a command that might override the system prompt. To build a robust enterprise LLM service, developers should focus on dynamic context management rather than static prompt optimization. Treating operational guidelines as external data via mock tool messages, rather than system instructions, provides a scalable way to reduce hallucinations and maintain high performance across hundreds of integrated services.

prompt-engineeringaillmrag+4
daangn

Karrot's Gen (opens in new tab)

Daangn has scaled its Generative AI capabilities from a few initial experiments to hundreds of diverse use cases by building a robust, centralized internal infrastructure. By abstracting model complexity and empowering non-technical stakeholders, the company has optimized API management, cost tracking, and rapid product iteration. The resulting platform ecosystem allows the organization to focus on delivering product value while minimizing the operational overhead of managing fragmented AI services. ### Centralized API Management via LLM Router Initially, Daangn faced challenges with fragmented API keys, inconsistent rate limits across teams, and the inability to track total costs across multiple providers like OpenAI, Anthropic, and Google. The LLM Router was developed as an "AI Gateway" to consolidate these resources into a single point of access. * **Unified Authentication:** Service teams no longer manage individual API keys; they use a unique Service ID to access models through the router. * **Standardized Interface:** The router uses the OpenAI SDK as a standard interface, allowing developers to switch between models (e.g., from Claude to GPT) by simply changing the model name in the code without rewriting implementation logic. * **Observability and Cost Control:** Every request is tracked by service ID, enabling the infrastructure team to monitor usage limits and integrate costs directly into the company’s internal billing platform. ### Empowering Non-Engineers with Prompt Studio To remove the bottleneck of needing an engineer for every prompt adjustment, Daangn built Prompt Studio, a web-based platform for prompt engineering and testing. This tool enables PMs and other non-developers to iterate on AI features independently. * **No-Code Experimentation:** Users can write prompts, select models (including internally served vLLM models), and compare outputs side-by-side in a browser-based UI. * **Batch Evaluation:** The platform includes an Evaluation feature that allows users to upload thousands of test cases to quantitatively measure how prompt changes impact output quality across different scenarios. * **Direct Deployment:** Once a prompt is finalized, it can be deployed via API with a single click. Engineers only need to integrate the Prompt Studio API once, after which non-engineers can update the prompt or model version without further code changes. ### Ensuring Service Reliability and Stability Because third-party AI APIs can be unstable or subject to regional outages, the platform incorporates several safety mechanisms to ensure that user-facing features remain functional even during provider downtime. * **Automated Retries:** The system automatically identifies retry-able errors and re-executes requests to mitigate temporary API failures. * **Region Fallback:** To bypass localized outages or rate limits, the platform can automatically route requests to different geographic regions or alternative providers to maintain service continuity. ### Recommendation For organizations scaling AI adoption, the Daangn model suggests that investing early in a centralized gateway and a no-code prompt management environment is essential. This approach not only secures API management and controls costs but also democratizes AI development, allowing product teams to experiment at a pace that is impossible when tied to traditional software release cycles.

prompt-engineeringaillmgen-ai+3
line

We held AI Campus Day to (opens in new tab)

LY Corporation recently hosted "AI Campus Day," a large-scale internal event designed to bridge the gap between AI theory and practical workplace application for over 3,000 employees. By transforming their office into a learning campus, the company successfully fostered a culture of "AI Transformation" through peer-led mentorship and task-specific experimentation. The event demonstrated that internal context and hands-on participation are far more effective than traditional external lectures for driving meaningful AI literacy and productivity gains. ## Hands-on Experience and Technical Support * The curriculum featured 10 specialized sessions across three tracks—Common, Creative, and Engineering—to ensure relevance for every job function. * Sessions ranged from foundational prompt engineering for non-developers to advanced technical topics like building Model Context Protocol (MCP) servers for engineers. * To ensure smooth execution, the organizers provided comprehensive "Session Guides" containing pre-configured account settings and specific prompt templates. * The event utilized a high support ratio, with 26 teaching assistants (TAs) available to troubleshoot technical hurdles in real-time and dedicated Slack channels for sharing live AI outputs. ## Peer-Led Mentorship and Internal Context * Instead of hiring external consultants, the program featured 10 internal "AI Mentors" who shared how they integrated AI into their actual daily workflows at LY Corporation. * Training focused exclusively on company-approved tools, including ChatGPT Enterprise, Gemini, and Claude Code, ensuring all demonstrations complied with internal security protocols. * Internal mentors were able to provide specific "company context" that external lecturers lack, such as integrating AI with existing proprietary systems and data. * A rigorous three-stage quality control process—initial flow review, final end-to-end dry run, and technical rehearsal—was implemented to ensure the educational quality of mentor-led sessions. ## Gamification and Cultural Engagement * The event was framed as a "festival" rather than a mandatory training, using campus-themed motifs like "enrollment" and "school attendance" to reduce psychological barriers. * A "Stamp Rally" system encouraged participation by offering tiered rewards, including welcome kits, refreshments, and subscriptions to premium AI tools. * Interactive exhibition booths allowed employees to experience AI utility firsthand, such as an AI photo zone using Gemini to generate "campus-style" portraits and an AI Agent Contest booth. * Strong executive support played a crucial role, with leadership encouraging staff to pause routine tasks for the day to focus entirely on AI experimentation and "playing" with new technologies. To effectively scale AI literacy within a large organization, it is recommended to move away from passive, one-size-fits-all lectures. Success lies in leveraging internal experts who understand the specific security and operational constraints of the business, and creating a low-pressure environment where employees can experiment with hands-on tasks relevant to their specific roles.

prompt-engineeringaigen-aigemini+5
line

Safety is a Given, Cost Reduction (opens in new tab)

AI developers often rely on system prompts to enforce safety rules, but this integrated approach frequently leads to "over-refusal" and unpredictable shifts in model performance. To ensure both security and operational efficiency, it is increasingly necessary to decouple safety mechanisms into separate guardrail systems that operate independently of the primary model's logic. ## Negative Impact on Model Utility * Integrating safety instructions directly into system prompts often leads to a high False Positive Rate (FPR), where the model rejects harmless requests alongside harmful ones. * Technical analysis using Principal Component Analysis (PCA) reveals that guardrail prompts shift the model's embedding results in a consistent direction toward refusal, regardless of the input's actual intent. * Studies show that aggressive safety prompting can cause models to refuse benign technical queries—such as "how to kill a Python process"—because the model adopts an overly conservative decision boundary. ## Positional Bias and Context Neglect * Research on the "Lost in the Middle" phenomenon indicates that LLMs are most sensitive to information at the beginning and end of a prompt, while accuracy drops significantly for information placed in the center. * The "Constraint Difficulty Distribution Index" (CDDI) demonstrates that the order of instructions matters; models generally follow instructions better when difficult constraints are placed at the beginning of the prompt. * In complex system prompts where safety rules are buried in the middle, the model may fail to prioritize these guardrails, leading to inconsistent safety enforcement depending on the prompt's structure. ## The Butterfly Effect of Prompt Alterations * Small, seemingly insignificant changes to a system prompt—such as adding a single whitespace, a "Thank you" note, or changing the output format to JSON—can alter more than 10% of a model's predictions. * Modifying safety-related lines within a unified system prompt can cause "catastrophic performance collapse," where the model's internal reasoning path is diverted, affecting unrelated tasks. * Because LLMs treat every part of the prompt as a signal that moves their decision boundaries, managing safety and task logic in a single string makes the system brittle and difficult to iterate upon. To build robust and high-performing AI applications, developers should move away from bloated system prompts and instead implement external guardrails. This modular approach allows for precise security filtering without compromising the model's creative or logical capabilities.

prompt-engineeringaillmprompt-injection+4
naver

Recreating the User's (opens in new tab)

The development of NSona, an LLM-based multi-agent persona platform, addresses the persistent gap between user research and service implementation by transforming static data into real-time collaborative resources. By recreating user voices through a multi-party dialogue system, the project demonstrates how AI can serve as an active participant in the daily design and development process. Ultimately, the initiative highlights a fundamental shift in cross-functional collaboration, where traditional role boundaries dissolve in favor of a shared starting point centered on AI-driven user empathy. ## Bridging UX Research and Daily Collaboration * The project was born from the realization that traditional UX research often remains isolated from the actual development cycle, leading to a loss of insight during implementation. * NSona transforms static user research data into dynamic "persona bots" that can interact with project members in real-time. * The platform aims to turn the user voice into a "live" resource, allowing designers and developers to consult the persona during the decision-making process. ## Agent-Centric Engineering and Multi-Party UX * The system architecture is built on an agent-centric structure designed to handle the complexities of specific user behaviors and motivations. * It utilizes a Multi-Party dialogue framework, enabling a collaborative environment where multiple AI agents and human stakeholders can converse simultaneously. * Technical implementation focused on bridging the gap between qualitative UX requirements and LLM orchestration, ensuring the persona's responses remained grounded in actual research data. ## Service-Specific Evaluation and Quality Metrics * The team moved beyond generic LLM benchmarks to establish a "Service-specific" evaluation process tailored to the project's unique UX goals. * Model quality was measured by how vividly and accurately it recreated the intended persona, focusing on the degree of "immersion" it triggered in human users. * Insights from these evaluations helped refine the prompt design and agent logic to ensure the AI's output provided genuine value to the product development lifecycle. ## Redefining Cross-Functional Collaboration * The AI development process reshaped traditional Roles and Responsibilities (RNR); designers became prompt engineers, while researchers translated qualitative logic into agentic structures. * Front-end developers evolved their roles to act as critical reviewers of the AI, treating the model as a subject of critique rather than a static asset. * The workflow shifted from a linear "relay" model to a concentric one, where all team members influence the product's core from the same starting point. To successfully integrate AI into the product lifecycle, organizations should move beyond using LLMs as simple tools and instead view them as a medium for interdisciplinary collaboration. By building multi-agent systems that reflect real user data, teams can ensure that the "user's voice" is not just a research summary, but a tangible participant in the development process.

prompt-engineeringaillmmultimodal-ai+5
kakao

[AI_TOP_1 (opens in new tab)

The AI TOP 100 contest was designed to shift the focus from evaluating AI model performance to measuring human proficiency in solving real-world problems through AI collaboration. By prioritizing the "problem-solving process" over mere final output, the organizers sought to identify individuals who can define clear goals and navigate the technical limitations of current AI tools. The conclusion of this initiative suggests that true AI literacy is defined by the ability to maintain a "human-in-the-loop" workflow where human intuition guides AI execution and verification. ### Core Philosophy of Human-AI Collaboration * **Human-in-the-Loop:** The contest emphasizes a cycle of human analysis, AI problem-solving, and human verification. This ensures that the human remains the "pilot" who directs the AI engine and takes responsibility for the quality of the result. * **Strategic Intervention:** Participants were encouraged to provide AI with structural context it might struggle to perceive (like complex table relationships) and to perform data pre-processing to improve AI accuracy. * **Task Delegation:** For complex iterative tasks, such as generating images for a montage, solvers were expected to build automated pipelines using AI agents to handle repetitive feedback loops while focusing human effort on higher-level strategy. ### Designing Against "One-Shot" Solutions * **Low Barrier, High Ceiling:** Problems were designed to be intuitive enough for anyone to understand but complex enough to prevent "one-shot" solutions (the "click-and-solve" trap). * **Targeting Technical Weaknesses:** Organizers intentionally embedded technical hurdles that current LLMs struggle with, forcing participants to demonstrate how they bridge the gap between AI limitations and a correct answer. * **The Difficulty Ladder:** To account for varying domain expertise (e.g., OCR experience), problems utilized a multi-part structure. This included "Easy" starting questions to build momentum and "Medium" hint questions that guided participants toward solving the more difficult "Killer" components. ### The 4-Pattern Problem Framework * **P1 - Insight (Analysis & Definition):** Identifying meaningful opportunities or problems within complex, unstructured data. * **P2 - Action (Implementation & Automation):** Developing functional code or workflows to execute a defined solution. * **P3 - Persuasion (Strategy & Creativity):** Generating logical and creative content to communicate technical solutions to non-technical stakeholders. * **P4 - Decision (Optimization):** Making optimal choices and simulations to maximize goals under specific constraints. ### Quality Assurance and Score Calibration * **4-Stage Pipeline:** Problems moved from Ideation to Drafting (testing for one-shot immunity), then to Candidate (analyzing abuse vulnerabilities), and finally to a Final selection based on difficulty balance. * **Cross-Model Validation:** Internal and alpha testers solved problems using various models including Claude, GPT, and Gemini to ensure that no single tool could bypass the intended human-led process. * **Effort-Based Scoring:** Instead of uniform points, scores were calibrated based on the "effort cost" and human competency required to solve them. This resulted in varying total points per problem to better reflect the true difficulty of the task. In the era of rapidly evolving AI, the ability to "use" a tool is becoming less valuable than the ability to "collaborate" with it. This shift requires a move toward building automated pipelines and utilizing a "difficulty ladder" approach to tackle complex, multi-stage problems that AI cannot yet solve in a single iteration.

prompt-engineeringaillmai-agent+4
line

A month-long task in just (opens in new tab)

This blog post explores how LY Corporation reduced a month-long development task to just five days by leveraging "vibe coding" with Generative AI tools like ChatGPT and Cursor. By shifting from traditional, rigid documentation to an iterative, demo-first approach, developers can rapidly validate multiple UI/UX solutions for complex problems like restaurant menu registration. The author concludes that AI's ability to handle frequent re-work makes it more efficient to "build fast and iterate" than to aim for perfection through long-form specifications. ### Strategic Shift to Rapid Prototyping * Traditional development cycles (spec → design → dev → fix) are often too slow to keep up with market trends due to heavy documentation and impact analysis. * The "vibe coding" approach prioritizes creating "working demos" over perfect specifications to find "good enough" answers through rapid feedback loops. * AI reduces the psychological and logistical burden of "starting over," allowing developers to refine the context and quality of outputs through repeated interaction without the friction of manual re-documentation. ### Defining Requirements and Solution Ideation * Initial requirements are kept minimal, focusing only on the core mission, top priorities, and essential data structures (e.g., product name, image, description) to avoid limiting AI creativity. * ChatGPT is used to generate a wide range of solution candidates, which are then filtered into five distinct approaches: Stepper Wizards, Live Previews with Quick Add, Template/Cloning, Chat Input, and OCR-based photo scanning. * This stage emphasizes volume and variety, using AI-generated pros and cons to establish selection criteria and identify potential UX bottlenecks early in the process. ### Detailed Design and Multi-Solution Wireframing * Each of the five chosen solutions is expanded into detailed screen flows and UI elements, such as progress bars, bottom sheets, and validation logic. * Prompt engineering is used iteratively; if an AI-generated result lacks a specific feature like "temporary storage" or "mandatory field validation," the prompt is adjusted to regenerate the design instantly. * The focus remains on defining the "what" (UI elements) and "how" (user flow) through textual descriptions before moving to actual coding. ### Implementation with Cursor and Flutter * Cursor is utilized to generate functional code based on the refined wireframes, using Flutter as the framework to ensure rapid cross-platform development for both iOS and Android. * The development follows a "skeleton-first" approach: first creating a main navigation hub with five entry points, then populating each individual solution module one by one. * Technical architecture decisions, such as using Riverpod for state management or SQLite for data storage, are layered onto the demo post-hoc, reversing the traditional "stack-first" development order to prioritize functional validation. ### Recommendation To maximize efficiency, developers should treat AI as a partner for high-speed iteration rather than a one-shot tool. By focusing on creating functional demos quickly and refining them through direct feedback, teams can bypass the bottlenecks of traditional software requirements and deliver user-centric products in a fraction of the time.

prompt-engineeringaillmgen-ai+5
line

Won't you become a hacker? (opens in new tab)

Hack Day 2025 serves as a cornerstone of LY Corporation’s engineering culture, bringing together diverse global teams to innovate beyond their daily operational scopes. By fostering a high-intensity environment focused on creative freedom, the event facilitates technical growth and strengthens interpersonal bonds across international branches. This 19th edition demonstrated how rapid prototyping and cross-functional collaboration can transform abstract ideas into functional AI-driven prototypes within a strict 24-hour window. ### Structure and Participation Dynamics * The hackathon follows a "9 to 9" format, providing exactly 24 hours of development time followed by a day for presentations and awards. * Participation is inclusive of all roles, including developers, designers, planners, and HR staff, allowing for holistic product development. * Teams can be "General Teams" from the same legal entity or "Global Mixed Teams" comprising members from different regions like Korea, Japan, Taiwan, and Vietnam. * The Developer Relations (DevRel) team facilitates team building for remote employees using digital collaboration tools like Zoom and Miro. ### AI-Powered Personality Analysis Project * The author's team developed a "Scouter" program inspired by Dragon Ball, designed to measure professional "combat power" based on communication history. * The system utilizes Slack bots and AI models to analyze message logs and map them to the Big 5 Personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism). * Professional metrics are visualized as game-like character statistics to make personality insights engaging and less intimidating. * While the original plan involved using AI to generate and print physical character cards, hardware failures with photo printers forced a technical pivot to digital file downloads. ### High-Pressure Presentation and Networking * Every team is allotted a strict 90-second window to pitch their product and demonstrate a live demo. * The "90-second rule" includes a mandatory microphone cutoff to maintain momentum and keep the large-scale event engaging for all attendees. * Dedicated booth sessions follow the presentations, allowing participants to provide hands-on experiences to colleagues and judges. * The event emphasizes "Perfect the Details," a core company value, by encouraging teams to utilize all available resources—from whiteboards to AI image generators—within the time limit. ### Environmental Support and Culture * The event occupies an entire office floor, providing a high-density yet comfortable environment designed to minimize distractions during the "Hack Time." * Cultural exchange is encouraged through "humanity snacks," where participants from different global offices share local treats in dedicated rest areas. * Strategic scheduling, such as "Travel Days" for international participants, ensures that teams can focus entirely on technical execution once the event begins. Participating in internal hackathons provides a vital platform for testing new technologies—like LLMs and personality modeling—that may not fit into immediate product roadmaps. For organizations with hybrid work models, these intensive in-person events are highly recommended to bridge the communication gap and build lasting trust between global teammates.

prompt-engineeringaigen-aihackathon+5
line

AI and the Writer’s Journey (opens in new tab)

LY Corporation is addressing the chronic shortage of high-quality technical documentation by treating the problem as an engineering challenge rather than a training issue. By utilizing Generative AI to automate the creation of API references, the Document Engineering team has transitioned from a "manual craftsmanship" approach to an "industrialized production" model. While the system significantly improves efficiency and maintains internal context better than generic tools, the team concludes that human verification remains essential due to the high stakes of API accuracy. ### Contextual Challenges with Generic AI Standard coding assistants like GitHub Copilot often fail to meet the specific documentation needs of a large organization. * Generic tools do not adhere to internal company style guides or maintain consistent terminology across projects. * Standard AI lacks awareness of internal technical contexts; for example, generic AI might mistake a company-specific identifier like "MID" for "Member ID," whereas the internal tool understands its specific function within the LY ecosystem. * Fragmented deployment processes across different teams make it difficult for developers to find a single source of truth for API documentation. ### Multi-Stage Prompt Engineering To ensure high-quality output without overwhelming the LLM's "memory," the team refined a complex set of instructions into a streamlined three-stage workflow. * **Language Recognition:** The system first identifies the programming language and specific framework being used. * **Contextual Analysis:** It analyzes the API's logic to generate relevant usage examples and supplemental technical information. * **Detail Generation:** Finally, it writes the core API descriptions, parameter definitions, and response value explanations based on the internal style guide. ### Transitioning to Model Context Protocol (MCP) While the prototype began as a VS Code extension, the team shifted to using the Model Context Protocol (MCP) to ensure the tool was accessible across various development environments. * Moving to MCP allows the tool to support multiple IDEs, including IntelliJ, which was a high-priority request from the developer community. * The MCP architecture decouples the user interface from the core logic, allowing the "host" (like the IDE) to handle UI interactions and parameter inputs. * This transition reduced the maintenance burden on the Document Engineering team by removing the need to build and update custom UI components for every IDE. ### Performance and the Accuracy Gap Evaluation of the AI-generated documentation showed strong results, though it highlighted the unique risks of documenting APIs compared to other forms of writing. * Approximately 88% of the AI-generated comments met the team's internal evaluation criteria. * The specialized generator outperformed GitHub Copilot in 78% of cases regarding style and contextual relevance. * The team noted that while a 99% accuracy rate is excellent for a blog post, a single error in a short API reference can render the entire document useless for a developer. To successfully implement AI-driven documentation, organizations should focus on building tools that understand internal business logic while maintaining a strict "human-in-the-loop" workflow. Developers should use these tools to generate the bulk of the content but must perform a final technical audit to ensure the precision that only a human author can currently guarantee.

prompt-engineeringaillmgen-ai+4
google

Deeper insights into retrieval augmented generation: The role of sufficient context (opens in new tab)

Google Research has introduced "sufficient context" as a critical new metric for evaluating Retrieval Augmented Generation (RAG) systems, arguing that simple relevance is an inadequate measure of performance. By focusing on whether a retrieved context contains all the necessary information to definitively answer a query, researchers developed an LLM-based autorater that classifies context sufficiency with 93% accuracy. This framework reveals that many RAG failures, specifically hallucinations, occur because models fail to abstain from answering when information is incomplete or contradictory. ## Defining and Measuring Sufficient Context * Sufficient context is defined as containing all information necessary to provide a definitive answer, while insufficient context is relevant but incomplete, inconclusive, or contradictory. * The researchers developed an "autorater" using Gemini 1.5 Pro, utilizing chain-of-thought prompting and 1-shot examples to evaluate query-context pairs. * In benchmarks against human expert "gold standard" labels, the autorater achieved 93% accuracy, outperforming specialized models like FLAMe (fine-tuned PaLM 24B) and NLI-based methods. * Unlike traditional metrics, this approach does not require ground-truth answers to evaluate the quality of the retrieved information. ## RAG Failure Modes and Abstention Challenges * State-of-the-art models (Gemini, GPT, Claude) perform exceptionally well when provided with sufficient context but struggle when context is lacking. * The primary driver of hallucinations in RAG systems is the "abstention" problem, where a model attempts to answer a query based on insufficient context rather than stating "I don't know." * Analyzing model responses through the lens of sufficiency allows developers to distinguish between "knowledge" (the model knows the answer internally) and "grounding" (the model correctly uses the provided context). ## Implementation in Vertex AI * The insights from this research have been integrated into the Vertex AI RAG Engine via a new LLM Re-Ranker feature. * The re-ranker prioritizes retrieved snippets based on their likelihood of providing a sufficient answer, significantly improving retrieval metrics such as normalized Discounted Cumulative Gain (nDCG). * By filtering for sufficiency during the retrieval phase, the system reduces the likelihood that the LLM will be forced to process misleading or incomplete data. To minimize hallucinations and improve the reliability of RAG applications, developers should move beyond keyword-based relevance and implement re-ranking stages that specifically evaluate context sufficiency. Ensuring that an LLM has the "right" to answer based on the provided data—and training it to abstain when that data is missing—is essential for building production-grade generative AI tools.

prompt-engineeringaillmgemini+5
google

Making complex text understandable: Minimally-lossy text simplification with Gemini (opens in new tab)

Google Research has introduced a novel system using Gemini models to perform minimally-lossy text simplification, a process designed to enhance readability while meticulously preserving original meaning and nuance. By utilizing an automated, iterative prompt-refinement loop, the system optimizes LLM instructions to achieve high-fidelity paraphrasing that avoids the information loss typical of standard summarization. A large-scale randomized study confirms that this approach significantly improves user comprehension across complex domains like law and medicine while simultaneously reducing cognitive load for the reader. ## Automated Evaluation and Fidelity Assessment * The system moves beyond traditional metrics like Flesch-Kincaid by using a Gemini-powered 1-10 readability scale that aligns more closely with human judgment and comprehension ease. * Fidelity is maintained through a specialized process using Gemini 1.5 Pro that maps specific claims from the original source text directly to the simplified output. * This mapping method identifies and weights specific error types, such as information loss, unnecessary gains, or factual distortions, to ensure the output remains a faithful representation of the technical original. ## Iterative Prompt Optimization Loop * To overcome the limitations and speed of manual prompt engineering, the researchers implemented a feedback loop where Gemini models optimize their own instructions. * In this "LLMs optimizing LLMs" setup, Gemini 1.5 Pro analyzes the performance of simplification prompts and proposes refinements based on automated readability and fidelity scores. * The optimization process ran for 824 iterations before performance plateaued, allowing the system to autonomously discover highly effective strategies for simplifying text without sacrificing detail. ## Validating Impact through Randomized Studies * The effectiveness of the model was validated with 4,563 participants across 31 diverse text excerpts covering specialized fields like aerospace, philosophy, finance, and biology. * The study utilized a randomized complete block design to compare the original text against simplified versions, measuring outcomes through nearly 50,000 multiple-choice question responses. * Beyond accuracy, researchers measured cognitive effort using the NASA Task Load Index and tracked self-reported user confidence to ensure the simplification actually lowered the barrier to understanding. This technology provides a scalable method for democratizing access to specialist knowledge by making expert-level discourse understandable to a general audience. The system is currently available as the "Simplify" feature within the Google app for iOS, offering a practical tool for users navigating complex digital information.

prompt-engineeringaillmgemini+3
google

Benchmarking LLMs for global health (opens in new tab)

Google Research has introduced a benchmarking pipeline and a dataset of over 11,000 synthetic personas to evaluate how Large Language Models (LLMs) handle tropical and infectious diseases (TRINDs). While LLMs excel at standard medical exams like the USMLE, this study reveals significant performance gaps when models encounter the regional context shifts and localized health data common in low-resource settings. The research concludes that integrating specific environmental context and advanced reasoning techniques is essential for making LLMs reliable decision-support tools for global health. ## Development of the TRINDs Synthetic Dataset * Researchers created a dataset of 11,000+ personas covering 50 tropical and infectious diseases to address the lack of rigorous evaluation data for out-of-distribution medical tasks. * The process began with "seed" templates based on factual data from the WHO, CDC, and PAHO, which were then reviewed by clinicians for clinical relevance. * The dataset was expanded using LLM prompting to include diverse demographic, clinical, and consumer-focused augmentations. * To test linguistic distribution shifts, the seed set was manually translated into French to evaluate how language changes impact diagnostic accuracy. ## Identifying Critical Performance Drivers * Evaluations of Gemini 1.5 models showed that accuracy on TRINDs is lower than reported performance on standard U.S. medical benchmarks, indicating a struggle with "out-of-distribution" disease types. * Contextual information is the primary driver of accuracy; the highest performance was achieved only when specific symptoms were combined with location and risk factors. * The study found that symptoms alone are often insufficient for an accurate diagnosis, emphasizing that LLMs require localized environmental data to differentiate between similar tropical conditions. * Linguistic shifts pose a significant challenge, as model performance dropped by approximately 10% when processing the French version of the dataset compared to the English version. ## Optimization and Reasoning Strategies * Implementing Chain-of-Thought (CoT) prompting—where the model is directed to explain its reasoning step-by-step—led to a significant 10% increase in diagnostic accuracy. * Researchers utilized an LLM-based "autorater" to scale the evaluation process, scoring answers as correct if the predicted diagnosis was meaningfully similar to the ground truth. * In tests regarding social biases, the study found no statistically significant difference in performance across race or gender identifiers within this specific TRINDs context. * Performance remained stable even when clinical language was swapped for consumer-style descriptions, suggesting the models are robust to variations in how patients describe their symptoms. To improve the utility of LLMs for global health, developers should prioritize the inclusion of regional risk factors and location-specific data in prompts. Utilizing reasoning-heavy strategies like Chain-of-Thought and expanding multilingual training sets are critical steps for bridging the performance gap in underserved regions.

prompt-engineeringaillmgemini+5