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Fiverr Pro
English
$
USD
I will audit your rag pipeline and fix why retrieval returns wrong answers
Gaurav S
About this gig
Your RAG app answers confidently and it is wrong. You have tried a bigger model and a better prompt, and it did not help - because the problem is almost never the model. It is that retrieval handed the model the wrong three paragraphs, and nothing in your pipeline noticed.
I find where that is happening and tell you what to change.
I have built a corrective-RAG pipeline as a LangGraph state machine that grades its own retrieval and refuses to answer from weak context, and I ship LLM services into production backends at a US investment bank and a US insurance provider.
WHAT YOU GET
A written report naming the actual failure - chunking strategy, embedding model, retrieval depth, missing reranking, or a query-formulation mismatch - with fixes ranked by effort against impact. Not a checklist. A diagnosis of your pipeline. Plus a live call to walk you through it.
WHAT I NEED FROM YOU
Repo access or the retrieval code, your chunking and embedding config, and 10-20 real questions where the answers are wrong.
Get to know Gaurav S
Gaurav S
Backend AI Systems Engineer
- FromIndia
- Member sinceFeb 2024
- Avg. response time2 hours
- Last delivery2 years
Languages
English, Hindi
Backend engineer with nearly four years of production experience building event-driven systems and shipping LLM-backed services into live products at a US investment bank and a US insurance provider.
I specialize in what most AI work skips: putting a model inside a service that already has traffic, tests and a release process it cannot break. Retrieval quality, evaluation harnesses, defined failure behavior.
Spring Boot, Go, Python, Kafka, Kubernetes, AWS, LangChain, LangGraph, RAG, vector databases, QLoRA.
Available full time.
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FAQ
Can you guarantee my accuracy will improve?
No, and be suspicious of anyone who does. I guarantee you will know exactly why it is failing and what to change. On Premium I implement the fixes and show you the before and after numbers - if they do not move, you still have the measurement showing where the problem actually is.
My pipeline is not LangChain. Does that matter?
No. LlamaIndex, Haystack, or hand-rolled is fine - I have built RAG from primitives without any framework. Retrieval quality problems are the same underneath.
Do you need my production data?
No. A representative sample and your real failing questions are enough. I will work against a redacted set if your data is sensitive.
What if my problem turns out to be generation, not retrieval?
The eval harness in Standard separates those two - that is precisely what context recall vs faithfulness measures. If it is generation, I will say so rather than sell you retrieval work.
Can you add features to my app?
Not in this gig. This is diagnosis, and on Premium the specific fixes found. New features are a separate custom offer.
