Tag: Prompt Engineering

  • LLM Evaluation: Stop AI Hallucinations with a Reliability Stack

    LLMs are impressive—until they confidently say something wrong.

    If you’ve built a chatbot, a support assistant, a RAG search experience, or an “agent” that takes actions, you’ve already met the core problem: hallucinations. And the uncomfortable truth is: you won’t solve it with a single prompt tweak.

    You solve it the same way you solve uptime or performance: with a reliability stack.

    This guide explains a practical approach to LLM evaluation that product teams can actually run every week—without turning into a research lab.

    TL;DR

    • Hallucinations are not a rare edge case; they’re a predictable failure mode.
    • The fix is not one trick—it’s a system: Test → Ground → Guardrail → Monitor.
    • You need an evaluation dataset (“golden set”) and automated checks before shipping.
    • RAG apps must evaluate retrieval quality and groundedness, not just “good answers”.
    • Production monitoring is mandatory: regressions will happen.

    Why LLMs hallucinate (quick explanation)

    LLMs predict the next token based on patterns in training data. They’re optimized to be helpful and fluent, not to be strictly factual.

    So when a user asks something ambiguous, something outside the model’s knowledge, something that requires exact policy wording, or something that depends on live data…the model may “fill in the blank” with plausible text.

    Your job isn’t to demand perfection. Your job is to build systems where wrong outputs become rare, detectable, and low-impact.

    The Reliability Stack (Test → Ground → Guardrail → Monitor)

    1) TEST: Build automated LLM evaluation before you ship

    Most teams “evaluate” by reading a few chats and saying “looks good.” That doesn’t scale.

    Step 1: Create an eval dataset (your “golden set”)

    Start with 50–100 real questions from your product or niche. Include:

    • top user intents (what you see daily)
    • high-risk intents (payments, security, health, legal)
    • known failures (copy from logs)
    • edge cases (missing info, conflicting context, weird phrasing)

    Each test case should have: Input (prompt + context), Expected behavior, and a Scoring method.

    Tip: Don’t force exact matching. Define behavior rules (must cite sources, must ask clarifying questions, must refuse when policy requires it, must call a tool instead of guessing).

    Step 2: Use 3 scoring methods (don’t rely on only one)

    A) Rule-based checks (fast, deterministic)

    • “Must include citations”
    • “Must not output personal data”
    • “Must return valid JSON schema”
    • “Must not claim certainty without evidence”

    B) LLM-as-a-judge (good for nuance)

    Use a judge prompt with a strict rubric to score: groundedness, completeness, and policy compliance.

    C) Human review (calibration + high-risk)

    • review a sample of passing outputs
    • review all high-risk failures
    • review new feature areas

    Step 3: Run evals for every change (like CI)

    Trigger your eval suite whenever you change the model, system prompt, retrieval settings, tools/function calling, safety filters, or routing logic. If scores regress beyond a threshold, block deploy.

    2) GROUND: Force answers to be traceable (especially for RAG)

    If correctness matters, the model must be grounded.

    Grounding method A: RAG (docs / KB)

    Common RAG failure modes: retrieval returns irrelevant docs, returns nothing, context is too long/noisy, docs are outdated.

    What to do: require answers only using retrieved context, require citations (doc id/URL), and if context is weak: ask clarifying questions or refuse.

    Grounding method B: Tools (APIs, DB queries)

    If the answer depends on live facts (pricing, account, inventory), don’t let the model guess—fetch data via tools and then summarize.

    Grounding method C: Constrained output formats

    If the LLM outputs code/SQL/JSON/tool calls: validate schema, reject unsafe actions, and add a repair step for formatting errors.

    3) GUARDRAILS: Reduce harm when the model is uncertain

    Guardrails aren’t “restricting AI.” They’re risk management.

    Guardrail A: “I don’t know” + escalation

    A safe assistant should admit uncertainty and offer a next step (search sources, ask for details, escalate to a human).

    Guardrail B: Mandatory citations in factual mode

    If it can’t cite sources, it should not claim facts. Offer general guidance and label it clearly.

    Guardrail C: Risk tiers by intent

    • Low risk: drafting, brainstorming, rewriting
    • Medium risk: troubleshooting, product policy
    • High risk: legal/medical/payments/security

    High risk needs stricter prompts, stronger grounding, and human handoff.

    Guardrail D: Tool permissioning (for agents)

    If an LLM can take actions: use allowlists, confirmations for destructive steps, rate limits, and audit logs.

    4) MONITOR: Production observability (where real failures show up)

    Even perfect test suites won’t catch everything. Your model will drift.

    Minimum logging (do this early)

    • prompt + system message version
    • model name/version
    • retrieved docs + scores (RAG)
    • tool calls + parameters
    • response
    • user feedback
    • latency + token cost

    (Redact sensitive content in logs.)

    Metrics that matter

    • Grounded answer rate: % answers with citations in factual mode
    • Escalation rate: how often the bot hands off
    • User satisfaction: feedback + resolution rate
    • Retrieval quality: % queries where top docs pass a relevance threshold
    • Regression alerts: eval score drops after changes

    LLM Evaluation Checklist (for teams)

    • Offline: eval dataset (50–200), automated checks, regression thresholds, versioned prompts/configs
    • Grounding: citations for factual mode, retrieval metrics, tool calls for live data
    • Guardrails: intent tiers, refusal + escalation path, tool permissions
    • Monitoring: logs with redaction, dashboards, regression alerts

    FAQ

    What is LLM evaluation?

    LLM evaluation is the process of testing an AI model’s outputs against a rubric (accuracy, safety, groundedness, format) using automated checks and human review.

    How do you reduce AI hallucinations?

    You reduce hallucinations with a reliability stack: automated tests, grounding (RAG/tools/citations), guardrails (refusal/escalation), and production monitoring.

    What is RAG evaluation?

    RAG evaluation checks whether retrieval returns the right documents and whether the final answer is grounded in those documents using citation and correctness scoring.

  • Why Agent Memory Is the Next Big AI Trend (And Why Long Context Isn’t Enough)

    AI is quickly shifting from chatbots to agents: systems that can plan, call tools, and complete tasks across apps. But there’s a major limitation holding agents back in real-world use:

    They don’t remember well.

    Without memory, agents repeat questions, forget preferences, lose context between sessions, and make inconsistent decisions. This is why agent memory is becoming one of the most important next trends in AI.

    What is “agent memory”?

    Agent memory is any system that allows an AI agent to persist and reuse information across time, beyond a single prompt window.

    Memory can include:

    • Facts about the user or organization (preferences, policies, configurations)
    • Past conversations and decisions (what was tried, what worked, what failed)
    • Task progress (plans, subtasks, intermediate outputs)
    • External state (documents, tickets, code changes, dashboards)

    The key idea is that an agent should not have to “relearn” everything in every conversation.

    Why long context windows are not enough

    It’s tempting to assume that bigger context windows solve memory. They help, but they don’t fully solve it for production systems.

    Common problems with “just stuff everything in context”:

    • Cost: sending large histories increases token usage and latency.
    • Noise: long histories contain irrelevant messages that distract the model.
    • Redundancy: repeated or similar interactions waste context space.
    • Weak retrieval: the model may miss the most important detail buried in a long transcript.
    • Security: you may not want to expose all historical data to every request.

    So the next step is not only bigger context — it’s better memory management.

    The modern memory pipeline (capture → compress → store → retrieve)

    Most practical memory systems follow a pipeline:

    1) Capture

    Record useful events from agent interactions, such as:

    • user preferences (tone, goals, tools used)
    • task outcomes (success/failure, links, artifacts)
    • important constraints (budget, policies, deadlines)

    2) Compress

    Convert raw chat logs into compact, structured memory units. Examples:

    • bullet summaries
    • key-value facts
    • decision records (“we chose X because Y”)

    3) Store

    Store memory in a system that supports retrieval. This might be:

    • a database table (structured facts)
    • a vector store (semantic recall)
    • a hybrid store (both structured + semantic)

    4) Retrieve (query-aware)

    At inference time, retrieve only what is relevant to the current goal. Retrieval can be based on:

    • semantic similarity (“this looks like a billing issue”)
    • filters (project, user, time window)
    • importance scoring (“critical policy constraints”)

    5) Consolidate (optional but powerful)

    Over time, you may merge related memories into higher-level summaries to reduce redundancy and improve reliability. This is similar to how humans form stable knowledge from repeated experiences.

    What agent memory enables (real examples)

    • Customer support agents that remember prior tickets, preferences, and recurring issues.
    • Coding agents that remember repo conventions, architecture decisions, and build/test commands.
    • Ops/SRE agents that remember incident timelines, previous fixes, and service-specific runbooks.
    • Personal assistants that remember schedules, communication style, and repeated tasks.

    How to start building agent memory (practical steps)

    1. Start small: store 20–100 important facts per user/project in a simple database.
    2. Add retrieval: fetch relevant facts based on the user’s request and the agent’s goal.
    3. Add summaries: compress long sessions into short “memory cards”.
    4. Measure quality: track whether memory reduces repeated questions and improves task completion.
    5. Add guardrails: don’t store secrets; add data retention rules; restrict what memories can be used.

    Why this is likely the next big AI layer

    Models keep improving, but many failures in agents come from missing context and inconsistent state. Memory systems are becoming the layer that turns a capable model into a reliable product.

    That’s why “agent memory” (and related ideas like memory consolidation and memory operating systems) is quickly becoming a major trend in AI development.