LLM audit, RAG optimization, and production AI—wrong answers, hallucinations, cost, latency. Evidence-based insights with measurable benchmarks.
43 posts in LLM Audit
Symptom
Pipeline
Measurement
Ops & Reliability
Format
Switching LLM vendors is not a model swap. It is a production migration across prompts, evals, routing, latency, cost, safety, and rollback. Use this checklist to change providers without losing quality or trust.
A practical incident postmortem template for production LLM and RAG teams: summary, impact, timeline, root cause, contributing factors, evidence, and action items you can actually ship.
AI Production Audit pricing only matters if scope, artifacts, and decision value are clear. This guide explains what you should expect at $3.8k, $9.8k, and a 4 to 6 week optimization sprint so you can choose the right engagement without wasting time or budget.
A real AI Production Audit should not end with vague recommendations. It should leave your team with sample findings, a usable scorecard, and a 30/60/90 roadmap clear enough for product, engineering, and finance to act on.
PDF tables break RAG in ways normal prose does not. OCR noise, missing column headers, merged cells, and row-boundary errors turn answer-bearing facts into weak retrieval units. This guide shows how to diagnose and fix table-specific failures before you blame embeddings or prompts.
Many RAG failures happen before semantic search, BM25, or reranking ever run. Wrong metadata filters silently exclude the right source by tenant, version, locale, product, plan, or freshness. This guide explains how metadata filters break recall, how to diagnose them, and the safer filter design patterns to ship.
Many RAG systems break right after docs change, not because the model got worse, but because indexing did not keep up. Stale chunks, mixed document versions, partial re-indexes, and weak invalidation create wrong answers after content updates. This guide explains how delta indexing fails, how to detect it, and the safer freshness patterns to ship.
Multilingual RAG often fails when the user asks in one language but the best source lives in another. Teams then overreact and split the corpus into separate indexes. This guide explains the real cross-language retrieval failure modes, how to diagnose them, and the safer patterns for one shared index with better recall.
Agent failures are often blamed on the model when the real problem sits in tool selection, argument generation, execution, state handling, or retry policy. This triage guide gives you a practical failure taxonomy, the minimum traces to inspect, and the fix order that moves production agent reliability fastest.
Many LLM features fail ROI reviews because teams show request volume and token spend instead of outcome economics. This playbook gives CTOs a practical way to frame cost per successful task, avoided cost, human rescue burden, and scale decisions before leadership kills the feature.
Chunking is one of the highest-leverage retrieval decisions in RAG. This guide explains how chunk size, overlap, and document-aware splitting change recall, precision, and groundedness, plus the evaluation method to choose a strategy instead of guessing.
Wrong RAG answers are not fixed by the same retrieval lever. Semantic search helps conceptual matching, hybrid search rescues literal recall, and reranking improves selection precision. This guide shows which one moves wrong answers fastest based on failure signals and eval data.
Model routing is one of the fastest ways to cut LLM cost, but only when it is treated as a measured policy instead of a blanket downgrade. This guide explains when to use small models, when large models should stay in the path, and how fallback rules keep quality intact.
Prompt injection in RAG is not just a prompt-writing problem. This playbook shows the minimum attack cases to test, the control layers to block server-side, and the decision logs you need to explain why risky requests were allowed, refused, or escalated.
Enterprise rollout raises the bar from 'mostly works' to 'predictably works under load, across cohorts, with rollback and evidence.' This checklist helps teams verify outcome reliability, retrieval and tool stability, latency budgets, release controls, observability, and operational ownership before expansion.
Cutting an OpenAI bill safely requires more than shortening prompts. This practical audit framework shows how to decompose spend, stop silent waste, reduce context, route cheaper models safely, and prove quality holds with before/after measurement.
Cost per token is accounting, not decision support. This guide shows how to calculate Cost per Successful AI Task, what to include in the numerator and denominator, how to segment by cohort, and how to avoid the measurement mistakes that hide real unit economics.
If the right document shows up in retrieval but the answer is still wrong, your problem is usually not recall. This article explains the post-retrieval failure modes that cause RAG hallucinations: weak selection, noisy context, source mixing, poor citation discipline, and missing refusal logic.
Before you change a model or prompt in production, you need more than one quality score. This framework shows what to measure across task success, groundedness, safety, cost, latency, tool behavior, and cohort-level regressions so you can ship changes with evidence.
The fastest way to build a useful eval set is to start from real user logs, not brainstormed prompts. This guide shows how to sample production traffic, redact safely, label outcomes, turn sessions into atomic test cases, and version a golden dataset that catches real regressions before release.
Classic app monitoring is not enough for production LLM systems. This guide explains the metrics, trace design, and failure taxonomy you need to make wrong answers, latency spikes, tool failures, and cost regressions diagnosable instead of mysterious.
Wrong answers in RAG systems are often retrieval, ranking, context construction, or freshness failures before they are prompt failures. This checklist helps teams prove the failing layer first, then fix with before/after baselines instead of prompt-tweaking roulette.
This production LLM audit checklist gives you 25 concrete signals that your AI system is leaking money, trust, or both. Use it to classify risk across quality, cost, latency, observability, release safety, and security before the next incident or budget review.
Caching is sold as a latency trick. In production, the bigger win is cost—if you don't silently ship wrong answers. This playbook shows cache layers, safe keys, invalidation beyond TTL, and correctness gates you can ship.
Dense embeddings are great at semantic similarity—and notoriously weak at exact-match recall. This playbook shows how to ship a hybrid retrieval stack (BM25 + vectors) with fusion + reranking to raise recall without blowing up latency or cost.
If you're iterating on prompts, swapping models, tuning tools, or tweaking retrieval, you're shipping production changes to a probabilistic system. This guide gives you a compact 50-test evaluation kit with schema, scoring, regression gates, and a 1-week rollout plan.
LLM cost almost never comes from one thing. We change the system: routing, retrieval policy, stop conditions, caching layers, tool reliability, and cost gates—and prove it with before/after benchmarks. The only metric that matters: Cost per Successful Task.
Wrong answers in RAG are rarely 'model problems.' They're usually system problems: retrieval misses, stale indexes, over-long context, weak citation discipline. This page explains what we actually change in a RAG optimization sprint—and the exact fix order that stops wrong answers fast.
RAG recall measures whether retrieval surfaces the right document. RAG precision measures how much retrieved context is relevant. This diagnostic helps you determine recall vs precision vs context construction—and what to fix first.
A production AI audit fails without observable evidence. The minimum logging/tracing schema that makes an audit worth paying for—without turning your system into a privacy or compliance disaster.
Bill climbing and nobody can explain why? Run a 45-minute token spend decomposition. This guide breaks spend into 4 buckets—base gen, context bloat, retries, tool loops—and shows which fixes cut cost fastest without killing quality.
Stop guessing. Wrong RAG answers fall into three buckets—recall, ranking, or context construction. This triage guide gives you 12 signals to classify failures in minutes, the minimum logging schema, and the fix order that actually moves the needle.
When teams say they need a 'GenAI audit,' they can mean wildly different things. This post clarifies the difference between a GenAI Audit and an AI System Audit, what artifacts you should expect, and how to prove impact with before/after evidence.
If you're shipping an LLM app in production, "it mostly works" is a trap. This post gives you 9 symptoms that indicate you need an audit, a 30-minute self-assessment template, and what a real audit should deliver.
Bottlenecks don't exist in isolation—they form chains. This step-by-step walkthrough shows how to map constraint chains in real production systems, from initial symptoms to root causes, using traces, metrics, and structured isolation.
A production baseline isn't a snapshot—it's a statistical model you can trust. This guide shows how to build baselines that account for traffic patterns, time-of-day effects, and variance, so you can detect real regressions instead of chasing noise.
Queues are a scalability primitive—not a dumping ground. This deep dive shows how bounded backpressure, priority isolation, idempotent workers, rate-limited execution, and failure-domain separation keep async workloads from becoming your tail-latency and incident generator.
Patterns don't create scalability. They address constraints. This catalog covers 12 patterns that repeatedly show up in systems that survive real load—what each pattern solves, when to use it, and when it backfires.
Stateless services aren't a style preference. They make compute replaceable—so autoscaling works, deployments are safe, and failures stay boring. Here's what "stateless" really means, where teams accidentally reintroduce state, and how to validate it under real load.
These terms get used interchangeably. In production they fail differently, use different metrics, and require different fixes. Here's a practical way to separate them and diagnose what you're actually dealing with.
Systems rarely collapse because you ran out of servers. They break when hidden constraints surface: pools exhaust, dependencies amplify tail latency, and data hotspots turn growth into incidents. This is what fails first when traffic jumps 10×—and how to prevent it.
Scalable architecture isn't "add more servers." It's a set of principles that keep systems predictable as traffic, data, and complexity grow. These nine rules show up repeatedly in architectures that survive production load.
Caching isn't a performance trick. It's where you choose to terminate load. This practical guide covers layered caching (Edge → Reverse Proxy → Redis), how each layer fails, and how to prevent cache stampedes from becoming outages.
Baseline cost/conv, quality, latency—isolate root causes, fix with measurable before/after benchmarks.
