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Nederlands
$
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I will audit and improve your rag retrieval system
Christopher O
Over deze dienst
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.
Maak kennis met Christopher O
Christopher O
Software engineer: AI systems, MCPs, public safety software
- Afkomstig uitVerenigde Staten
- Lid sindsdec 2014
- Gem. reactietijd1 uur
Talen
Engels
I build AI systems that run in production, not demos that work once.
Sixteen years in enterprise IT, security operations, and observability engineering. I have been a volunteer firefighter and EMR since 2019 and build software for emergency services, so I take reliability seriously.
I work on retrieval systems, agent architectures, and MCP servers. I built a hybrid retrieval engine from scratch, with no hosted vector database.
You get architecture you can read, tests that prove it works, and cost analysis before you commit to a model. I work async and am direct about scope.
Mijn portfolio
Veelgestelde vragen
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.
