LLM Error Handling and Validation Techniques

A good litmus test for whether an engineer has worked with AI in production systems is a simple question: how do you ensure safe JSON output? If you’re using AI to make code-level decisions at runtime, you’ll need its conclusions in a machine-readable form. JSON is the most common of these. But since LLMs are generative, they’re not guaranteed to output valid JSON, even when asked. You might get triple backticks. Single quotes instead of double. Unescaped characters. A friendly preamble before the JSON. A refusal to answer. Or the model might just ignore the instruction entirely. Something in the user’s prompt might throw it off. The list goes on. Engineers who’ve worked with this in production know the edge cases - the strange behaviours, brittle responses, and unexpected failures. An inexperienced engineer might suggest “just ask for JSON” or “just use JSON mode”, but that’s not enough. We’ve seen invalid output from models even in JSON mode, and not all models support it anyway. There are three techniques we’ve come to rely on that now work so well, we don’t even use JSON mode anymore: prefill, repair, and tenacity. Prefill the assistant’s response with the opening of the JSON you want — this is by far the most reliable technique we’ve found. Repair the output with a library like json-repair, handling common cases like XML wrappers or markdown fences. Catch all parsing errors and retry with the same prompt using a library like tenacity. Because LLMs are non-deterministic, a second attempt will often succeed where the first didn’t. We’ve been using this setup in production for over six months now. We haven’t seen a single JSON error in that time.

𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐚𝐫𝐞 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥 - 𝐛𝐮𝐭 𝐭𝐡𝐞𝐲 𝐚𝐥𝐬𝐨 𝐛𝐫𝐞𝐚𝐤 𝐢𝐧 𝐬𝐮𝐫𝐩𝐫𝐢𝐬𝐢𝐧𝐠 𝐰𝐚𝐲𝐬. As agentic systems become more complex, multi-step, and tool-driven, understanding why they fail (and how to fix it) becomes critical for anyone building reliable AI workflows. This framework highlights the 10 most common failure modes in AI agents and the practical fixes that prevent them: - 𝐇𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐞𝐝 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 Agents invent steps, facts, or assumptions. Fix: Add grounding (RAG), verification steps, and critic agents. - 𝐓𝐨𝐨𝐥 𝐌𝐢𝐬𝐮𝐬𝐞 Agents pick the wrong tool or misinterpret outputs. Fix: Provide clear schemas, examples, and post-tool validation. - 𝐈𝐧𝐟𝐢𝐧𝐢𝐭𝐞 𝐨𝐫 𝐋𝐨𝐧𝐠 𝐋𝐨𝐨𝐩𝐬 Agents refine forever without reaching “good enough.” Fix: Add iteration limits, stopping rules, or watchdog agents. - 𝐅𝐫𝐚𝐠𝐢𝐥𝐞 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 Plans collapse after a single failure. Fix: Insert step checks, partial output validation, and re-evaluation rules. - 𝐎𝐯𝐞𝐫-𝐃𝐞𝐥𝐞𝐠𝐚𝐭𝐢𝐨𝐧 Agents hand off tasks endlessly, creating runaway chains. Fix: Use clear role definitions and ownership boundaries. - 𝐂𝐚𝐬𝐜𝐚𝐝𝐢𝐧𝐠 𝐄𝐫𝐫𝐨𝐫𝐬 Small early mistakes compound into major failures. Fix: Insert verification layers and checkpoints throughout the task. - 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐎𝐯𝐞𝐫𝐟𝐥𝐨𝐰 Agents forget earlier steps or lose track of conversation state. Fix: Use episodic + semantic memory and frequent summaries. - 𝐔𝐧𝐬𝐚𝐟𝐞 𝐀𝐜𝐭𝐢𝐨𝐧𝐬 Agents attempt harmful, risky, or unintended behaviors. Fix: Add safety rails, sandbox access, and allow/deny lists. - 𝐎𝐯𝐞𝐫-𝐂𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐢𝐧 𝐁𝐚𝐝 𝐎𝐮𝐭𝐩𝐮𝐭𝐬 LLMs answer incorrectly with total confidence. Fix: Add confidence estimation prompts and critic–verifier loops. - 𝐏𝐨𝐨𝐫 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐂𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐢𝐨𝐧 Agents argue, duplicate work, or block each other. Fix: Add role structure, shared workflows, and central orchestration. Reliable AI agents are not created by prompt engineering alone - they are created by systematically eliminating failure modes. When guardrails, memory, grounding, validation, and coordination are all designed intentionally, agentic systems become far more stable, predictable, and trustworthy in real-world use. ♻️ Repost this to help your network get started ➕ Follow Prem N. for more

✨ 𝗟𝗟𝗠𝘀 𝗱𝗼𝗻’𝘁 𝗳𝗮𝗶𝗹 𝗺𝘆𝘀𝘁𝗲𝗿𝗶𝗼𝘂𝘀𝗹𝘆; they fail in repeatable, diagnosable ways. 🧪 Great demos die in prod. I published A Field Guide to LLM Failure Modes: minimal repros, code, and production guardrails for leaders who own SLAs, budgets, and audits. 📦 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻𝘀𝗶𝗱𝗲: 🧠 Behavior & reasoning: hallucinations, lost‑in‑the‑middle, long‑context illusions, multi‑step fragility. 🔎 RAG & prompting: chunking/overlap, embedding drift, stale indexes, recall@k targets. 🧰 Structured I/O & tools: schema‑enforced JSON, function calling, argument/type checks. 🛡️ Safety & security: jailbreaks/prompt injection, calibrated refusals, agent loop limits, least‑privilege tools. 📜 Data & governance: contamination, memorization/PII, provenance, RTBF, export controls. ⚙️ Ops: cost/latency/throughput budgets, seed‑stability harnesses, full telemetry (prompts, tokens, traces), rollback gates. 🧭 𝗨𝘀𝗲 𝗶𝘁 𝗮𝘀 𝗮 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸: diagnose → mitigate → validate → monitor. 🚢 Ship with budget gates, schema conformance checks, recall@k monitors, red‑team suites, and trace IDs on every answer. Move from polished demo to dependable system—without sprawl or surprise bills. 🔗 Read: A Field Guide to LLM Failure Modes https://lnkd.in/eFN8Ac38 #AI #LLM #GenAI #MLOps #AIOps #Security #DataGovernance #ProductEngineering #GenerativeAI

Tired of your LLM just repeating the same mistakes when retries fail? Simple retry strategies often just multiply costs without improving reliability when models fail in consistent ways. You've built validation for structured LLM outputs, but when validation fails and you retry the exact same prompt, you're essentially asking the model to guess differently. Without feedback about what went wrong, you're wasting compute and adding latency while hoping for random success. A smarter approach feeds errors back to the model, creating a self-correcting loop. Effective AI Engineering #13: Error Reinsertion for Smarter LLM Retries 👇 The Problem ❌ Many developers implement basic retry mechanisms that blindly repeat the same prompt after a failure: [Code example - see attached image] Why this approach falls short: - Wasteful Compute: Repeatedly sending the same prompt when validation fails just multiplies costs without improving chances of success. - Same Mistakes: LLMs tend to be consistent - if they misunderstand your requirements the first time, they'll likely make the same errors on retry. - Longer Latency: Users wait through multiple failed attempts with no adaptation strategy.Beyond Blind Repetition: Making Your LLM Retries Smarter with Error Feedback. - No Learning Loop: The model never receives feedback about what went wrong, missing the opportunity to improve. The Solution: Error Reinsertion for Adaptive Retries ✅ A better approach is to reinsert error information into subsequent retry attempts, giving the model context to improve its response: [Code example - see attached image] Why this approach works better: - Adaptive Learning: The model receives feedback about specific validation failures, allowing it to correct its mistakes. - Higher Success Rate: By feeding error context back to the model, retry attempts become increasingly likely to succeed. - Resource Efficiency: Instead of hoping for random variation, each retry has a higher probability of success, reducing overall attempt count. - Improved User Experience: Faster resolution of errors means less waiting for valid responses. The Takeaway Stop treating LLM retries as mere repetition and implement error reinsertion to create a feedback loop. By telling the model exactly what went wrong, you create a self-correcting system that improves with each attempt. This approach makes your AI applications more reliable while reducing unnecessary compute and latency.

You’re in an AI Engineer interview. Interviewer: Your RAG system retrieves the right documents, but the generated answer still hallucinates. How would you detect and reduce hallucinations before returning the response? Here’s how I would approach it. First, I would verify whether the generated answer is actually grounded in the retrieved context. 1️⃣Context verification Run a verification step where another LLM (or the same model) checks whether every claim in the answer is supported by the retrieved documents. If a statement cannot be traced back to the context, it gets flagged or removed. 2️⃣Citation-based generation Force the model to produce answers with citations to the retrieved chunks. If the model cannot point to a source, that part of the answer is likely hallucinated. 3️⃣Answer validation / re-ranking Generate multiple candidate answers and use a cross-encoder or verifier model to score how well each answer aligns with the retrieved context. 4️⃣Constrained prompting Explicitly instruct the model to answer only from the provided context. If the information is missing, the model should say it doesn’t know. What this really does is introduce a verification layer between retrieval and the final response. Instead of a simple pipeline: Retrieve -> Generate You now have a much safer system: Retrieve -> Generate -> Verify In production AI systems, retrieval alone is not enough. Grounding is everything. #ai #llm #rag #aiengineering #datascience Follow Sneha Vijaykumar for more...😊

Are your LLM apps still hallucinating? Zep used to as well—a lot. Here’s how we worked to solve Zep's hallucinations. We've spent a lot of cycles diving into why LLMs hallucinate and experimenting with the most effective techniques to prevent it. Some might sound familiar, but it's the combined approach that really moves the needle. First, why do hallucinations happen? A few core reasons: 🔍 LLMs rely on statistical patterns, not true understanding. 🎲 Responses are based on probabilities, not verified facts. 🤔 No innate ability to differentiate truth from plausible fiction. 📚 Training datasets often include biases, outdated info, or errors. Put simply: LLMs predict the next likely word—they don’t actually "understand" or verify what's accurate. When prompted beyond their knowledge, they creatively fill gaps with plausible (but incorrect) info. ⚠️ Funny if you’re casually chatting—problematic if you're building enterprise apps. So, how do you reduce hallucinations effectively? The #1 technique: grounding the LLM in data. - Use Retrieval-Augmented Generation (RAG) to anchor responses in verified data. - Use long-term memory systems like Zep to ensure the model is always grounded in personalization data: user context, preferences, traits etc - Fine-tune models on domain-specific datasets to improve response consistency and style, although fine-tuning alone typically doesn't add substantial new factual knowledge. - Explicit, clear prompting—avoid ambiguity or unnecessary complexity. - Encourage models to self-verify conclusions when accuracy is essential. - Structure complex tasks with chain-of-thought prompting (COT) to improve outputs or force "none"/unknown responses when necessary. - Strategically tweak model parameters (e.g., temperature, top-p) to limit overly creative outputs. - Post-processing verification for mission-critical outputs, for example, matching to known business states. One technique alone rarely solves hallucinations. For maximum ROI, we've found combining RAG with a robust long-term memory solution (like ours at Zep) is the sweet spot. Systems that ground responses in factual, evolving knowledge significantly outperform. Did I miss any good techniques? What are you doing in your apps?

OpenAI CPO: Evals are becoming a core skill for PMs. PM in 2025 is changing fast. PMs need to learn brand new skills: 1. AI Evals (https://lnkd.in/eGbzWMxf) 2. AI PRDs (https://lnkd.in/eMu59p_z) 3. AI Strategy (https://lnkd.in/egemMhMF) 4. AI Discovery (https://lnkd.in/e7Q6mMpc) 5. AI Prototyping (https://lnkd.in/eJujDhBV) And evals is amongst the deepest topics. There's 3 steps to them: 1. Observing (https://lnkd.in/e3eQBdMp) 2. Analyzing Errors (https://lnkd.in/eEG83W5D) 3. Building LLM Judges (https://lnkd.in/ez3stJRm) - - - - - - Here's your simple guide to evals in 5 minutes: (Repost this before anything else ♻️) 𝟭. 𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 Start with 100 diverse traces of your LLM pipeline. Use real data if you can, or systematic synthetic data generation across key dimensions if you can't. Quality over quantity here: aggressive filtering beats volume. 𝟮. 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗢𝗽𝗲𝗻 𝗖𝗼𝗱𝗶𝗻𝗴 Read every trace carefully and label failure modes without preconceptions. Look for the first upstream failure in each trace. Continue until you hit theoretical saturation, when new traces reveal no fundamentally new error types. 𝟯. 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗬𝗼𝘂𝗿 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗠𝗼𝗱𝗲𝘀 Group similar failures into coherent, binary categories through axial coding. Focus on Gulf of Generalization failures (where clear instructions are misapplied) rather than Gulf of Specification issues (ambiguous prompts you can fix easily). 𝟰. 𝗕𝘂𝗶𝗹𝗱 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗼𝗿𝘀 Create dedicated evaluators for each failure mode. Use code-based checks when possible (regex, schema validation, execution tests). For subjective judgments, build LLM-as-Judge evaluators with clear Pass/Fail criteria, few-shot examples, and structured JSON outputs. 𝟱. 𝗗𝗲𝗽𝗹𝗼𝘆 𝘁𝗵𝗲 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗙𝗹𝘆𝘄𝗵𝗲𝗲𝗹 Integrate evals into CI/CD, monitor production with bias-corrected success rates, and cycle through Analyze→ Measure→ Improve continuously. New failure modes in production feed back into your evaluation artifacts. Evals are now a core skill for AI PMs. This is your map. - - - - - I learned this from Hamel Husain and Shreya Shankar. Get 35% off their course: https://lnkd.in/e5DSNJtM 📌 Want our step-by-step guide to evals? Comment 'steps' + DM me. Repost to cut the line. ➕ Follow Aakash Gupta to stay on top of AI x PM.

Building LLM apps? Learn how to test them effectively and avoid common mistakes with this ultimate guide from LangChain! 🚀 This comprehensive document highlights: 1️⃣ Why testing matters: Tackling challenges like non-determinism, hallucinated outputs, and performance inconsistencies. 2️⃣ The three stages of the development cycle: 💥 Design: Incorporating self-corrective mechanisms for error handling (e.g., RAG systems and code generation). 💥Pre-Production: Building datasets, defining evaluation criteria, regression testing, and using advanced techniques like pairwise evaluation. 💥Post-Production: Monitoring performance, collecting feedback, and bootstrapping to improve future versions. 3️⃣ Self-corrective RAG applications: Using error handling flows to mitigate hallucinations and improve response relevance. 4️⃣ LLM-as-Judge: Automating evaluations while reducing human effort. 5️⃣ Real-time online evaluation: Ensuring your LLM stays robust in live environments. This guide offers actionable strategies for designing, testing, and monitoring your LLM applications efficiently. Check it out and level up your AI development process! 🔗📘 ------------ Add your thoughts in the comments below—I’d love to hear your perspective! Sarveshwaran Rajagopal #AI #LLM #LangChain #Testing #AIApplications

One of the most significant challenges faced by AI developers is ensuring the accuracy, relevance, and quality of outputs generated by LLMs. Guardrails is an open-source package addressing this gap, enhancing the reliability and structure of LLM outputs. Main features: (1) Pydantic-style validation ensures the output from your LLM is not only accurate but also adheres to specified structures and variable types (2) A corrective action is triggered if the LLM output doesn't meet your success criteria, e.g. firing another LLM call with a refined prompt or raising an exception for custom handling (3) Supports streaming so users won’t have to wait for validations to finalize (4) Compatible with various LLMs including OpenAI's GPT, Anthropic's Claude, and and any language model on Hugging Face Any developer moving from their Twitter demo into a production setting implemented a Guardrails-like solution. I wish I had tinkered with this package earlier—it would have saved me the effort of coding it myself. I address the topic of increasing LLM factually in one of my recent deep dives covering research-backed prompting techniques https://lnkd.in/g7_6eP6y I also cover other techniques to prevent prompt injection attacks in my last post https://lnkd.in/gSe-3_Qr Guardrails AI GitHub repo https://lnkd.in/gZ-Pk5PS Tag anyone who was looking for more accurate LLM responses.

𝐃𝐢𝐝 𝐲𝐨𝐮 𝐤𝐧𝐨𝐰 𝐋𝐋𝐌 𝐡𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧𝐬 𝐜𝐚𝐧 𝐛𝐞 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐝 𝐢𝐧 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞? In a recent post, I talked about why hallucinations happen in LLMs and how they affect different AI applications. While creative fields may welcome hallucinations as a way to spark out-of-the-box thinking, business use cases don’t have that flexibility. In industries like healthcare, finance, or customer support, hallucinations can’t be overlooked. Accuracy is non-negotiable, and catching unreliable LLM outputs in real-time becomes essential. So, here’s the big question: 𝐇𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐦𝐨𝐧𝐢𝐭𝐨𝐫 𝐟𝐨𝐫 𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠 𝐚𝐬 𝐜𝐨𝐦𝐩𝐥𝐞𝐱 𝐚𝐬 𝐡𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧𝐬? That’s where the 𝐓𝐫𝐮𝐬𝐭𝐰𝐨𝐫𝐭𝐡𝐲 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥 (𝐓𝐋𝐌) steps in. TLM helps you detect LLM errors/hallucinations by scoring the trustworthiness of every response generated by 𝐚𝐧𝐲 LLM.  This comprehensive trustworthiness score combines factors like data-related and model-related uncertainties, giving you an automated system to ensure reliable AI applications. 🏁 The benchmarks are impressive. TLM reduces the rate of incorrect answers from OpenAI’s o1-preview model by up to 20%. For GPT-4o, that reduction goes up to 27%. On Claude 3.5 Sonnet, TLM achieves a similar 20% improvement. Here’s how TLM changes the game for LLM reliability: 1️⃣ For Chat, Q&A, and RAG applications: displaying trustworthiness scores helps your users identify which responses are unreliable, so they don’t lose faith in the AI. 2️⃣ For data processing applications (extraction, annotation, …): trustworthiness scores help your team identify and review edge-cases that the LLM may have processed incorrectly. 3️⃣ The TLM system can also select the most trustworthy response from multiple generated candidates, automatically improving the accuracy of responses from any LLM. With tools like TLM, companies can finally productionize AI systems for customer service, HR, finance, insurance, legal, medicine, and other high-stakes use cases.  Kudos to the Cleanlab team for their pioneering research to advance the reliability of AI. I am sure you want to learn more and use it yourself, so I will add reading materials in the comments!