LLM App Review
Find out what is actually broken before you rebuild anything.
Most LLM applications that struggle in production share a pattern: the team can see the bad outputs but cannot see why they happen. Is it retrieval? The prompt? The model? Context truncation? A review answers that question with evidence.
What we examine
- Architecture — how requests flow through your chains, agents, and services; where state lives; what happens on partial failure.
- Prompt inventory — every prompt in the system, its version history (if any), and how changes reach production.
- Retrieval quality — what your pipeline actually returns for real queries: chunking fit, embedding choice, filter correctness, and whether the right passage is even in the candidate set.
- Cost and latency profile — token spend per request path, cache behavior, model selection, and p50/p95 latency, measured from traces rather than estimated.
- Failure-mode inventory — a categorized catalog of observed failures from your logs and traces: hallucinated citations, refusal loops, truncated context, tool-call errors, formatting drift.
What you get
A written report with findings ranked by user impact and effort, each tied to the evidence behind it, and a hardening plan sequenced so the highest-impact fixes come first. Where a finding is measurable, we include the baseline number so you can verify the fix later.
The report is designed to be executed by your team. Some clients take it and run; others ask us to stay and do the work. Both are fine — the review stands on its own.
What we need from you
Read access to the codebase, a sample of production traces or logs, and a few hours with the engineers who own the system. Reviews are scoped in an initial conversation and billed hourly like all our work.