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Fiverr Pro
English
$
USD
I will audit and improve your rag retrieval system
Christopher O
About this gig
Your RAG system returns answers, but do you know whether it retrieves the right evidence?
I audit and improve existing retrieval systems using measurable evaluation instead of prompt guesswork. I can examine ingestion, chunk boundaries, metadata, filters, dense or hybrid search, reranking, context assembly, citations, latency, and cost.
Depending on the package, you receive:
- A representative evaluation dataset
- Retrieval metrics when available labels support them
- Citation and source-coverage checks
- Failure analysis by query type
- Cost and latency measurements
- Targeted implementation changes and regression tests
- A reproducible before-and-after report
I built a hybrid retrieval engine from scratch without a hosted vector database, so I understand retrieval below the framework layer.
This gig is for an existing RAG, semantic-search, or document-retrieval system. It is not a generic chatbot build. Results depend on your corpus, labels, models, and infrastructure; I report measured outcomes and tradeoffs, not guaranteed accuracy.
Message me with your stack and approximate corpus scope before ordering.
Get to know Christopher O
Christopher O
Security minded AI dev who also runs into burning buildings
- FromUnited States
- Member sinceDec 2014
- Avg. response time2 hours
Languages
English
I'm Christopher. I build websites and the AI behind them, in production, not demos that work once.
16+ years in enterprise IT, security ops, observability. Volunteer firefighter and EMR since 2019, writing software for first responders. When your code is what someone grabs on their worst day, reliability is not a feature request.
Web apps, landing pages, retrieval systems, agent architectures, MCP servers, security hardening. Retrieval engine built from scratch, no hosted vector DB.
You get architecture you can read, tests that prove it, and costs up front. Async, blunt about scope.
My Portfolio
FAQ
Do you build a new chatbot or RAG app from scratch?
No. This gig audits and improves an existing retrieval pipeline. A new application requires a custom offer.
Which retrieval systems can you review?
Custom pipelines and systems using tools such as LangChain, LlamaIndex, pgvector, Elasticsearch, OpenSearch, Pinecone, Qdrant, Weaviate, or Chroma.
Do I need an evaluation dataset already?
No. I can structure one from supplied questions, documents, logs, and known failures. You must validate domain-specific relevance judgments.
Which metrics will you use?
Depending on labels and behavior: Recall@K, Precision@K, MRR, nDCG, citation coverage, latency percentiles, and estimated cost per query.
Can you guarantee an accuracy improvement?
No. I provide reproducible measurements, bounded changes, and documented tradeoffs. Outcomes depend on the corpus, models, labels, and infrastructure.
What counts as one improvement area?
One bounded area such as chunking, query processing, retrieval strategy, metadata filtering, reranking, context assembly, or citation validation.
Can you work with confidential data?
Yes when access is safe and lawful. Use sanitized samples or approved repository access. Never send production secrets or regulated records through Fiverr chat.
