LLM returning wrong answers in production.Expert in 10 minutes.

PM assigned in 10 minutes. A GenAI engineer starts diagnosing within the hour examining your prompt templates, retrieval pipeline, and context window. LLM hallucinations are almost always caused by one of three things: vague system prompts that don't constrain the model, poor retrieval in RAG pipelines returning irrelevant chunks, or missing guardrails that let the model speculate beyond its knowledge. The engineer traces the root cause in the first session and ships the fix with before-and-after accuracy scores so you can see exactly what changed.

The PM diagnoses this in the first 15 minutes by running your failing queries through each layer of your pipeline separately. If the LLM answers correctly when given the right context directly, it is a retrieval problem wrong chunks, bad embeddings, or a broken similarity search. If it still hallucinate with correct context, the issue is the prompt or the model's instruction-following. We trace it systematically: test with golden context first, then with your live retrieval, then with your full system prompt chain. You get a written diagnosis with the exact failure point before the fix starts.

Yes. Every LLM fix is evaluated before deployment. The engineer builds or runs a RAGAS evaluation suite Retrieval Augmented Generation Assessment Score measuring faithfulness, context precision, and answer relevance on a test set of your real queries. Before-and-after scores are delivered with the fix so you see exactly what improved. For production hallucination fixes, we also include a regression test set of known bad queries so you catch regressions automatically in future deployments when prompts or models change.

The PM delivers a regression test suite and monitoring recommendations so you detect regressions before users do. This includes: a set of adversarial test queries that surface hallucinations, a recommended eval run as part of your CI/CD pipeline, and guardrail logic (output validation, confidence thresholds, or a second LLM judge) that catches bad outputs at runtime. If hallucinations return within 7 days of the fix, PM flags it immediately and the team diagnoses whether it is a data drift issue, a model update, or a prompt regression.

PM delivers a written session summary covering: exact root cause identified, changes made to prompts/retrieval/guardrails, before-and-after RAGAS evaluation scores, regression test set for your CI pipeline, and monitoring recommendations. All code changes are pushed to your repo as a PR nothing is deployed without your approval. You own everything: prompts, eval scripts, guardrails. No proprietary tooling lock-in. If the issue recurs within 7 days, PM escalates immediately at no extra charge for the diagnosis call.