RAG LLM App Reliability Review
Focused paid review for Python RAG and LLM applications: FastAPI endpoints, crawler-to-context flow, vector store wiring, prompt calls, attachment handling, caching, secret boundaries, observability, and deployment readiness.
$149 focused app review
Book the review · Ask a pre-sales question · View sample review · my-rag repo
What you get
- A focused review of one RAG/LLM app, crawler, vector-store path, prompt endpoint, or deployment blocker.
- A practical note covering data ingestion, context construction, token/secret handling, retrieval checks, cache boundaries, model-call failure modes, and API response behavior.
- A prioritized checklist for making the app safer to test, run, and iterate without leaking secrets or hiding failures.
Best fit
- You have a Python RAG, crawler, or LLM API that works once but is fragile across credentials, source data, prompts, or deployments.
- You are debugging FastAPI, vector DB, object storage, crawler login, prompt quality, retries, or response packaging.
- You can share a public repo, sanitized logs, prompt shape, retrieval examples, or a concrete reproduction.
Boundary: this is remote engineering review and setup guidance. It does not include managed LLM operation, secret handling, guaranteed answer accuracy, data scraping authorization, or custom feature delivery.