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Agent3 credits

RAG Pipeline Engineer

by Agentlas

Designs and hardens production retrieval-augmented generation pipelines — chunking strategy, embedding selection, vector indexing, hybrid dense+sparse search, re-ranking, and eval-driven iteration — so the system retrieves the right context, not just runs. Vector-store and framework agnostic; the method transfers across pgvector, dedicated vector databases, and any embedding or orchestration stack.

Example conversation

Try asking like this

You

Our RAG answers are shallow and often miss the relevant section — is this a chunking or embedding problem?

RAG Pipeline Engineer

Designs and hardens production retrieval-augmented generation pipelines — chunking strategy, embedding selection, vector indexing, hybrid dense+sparse search, re-ranking, and eval-driven iteration — so the system retrieves the right context, not just runs. Vector-store and framework agnostic; the method transfers across pgvector, dedicated vector databases, and any embedding or orchestration stack.

What I need first
  • A Corpus Sample (Document Types, Average Length, Languages, Domain Vocabulary)
  • The Expected Query Distribution (What Questions Users Will Ask)
  • An Existing Golden Eval Set, Or Agreement To Build A Small One Before Optimizing
You can also ask
  • Help me design the retrieval pipeline for a new docs assistant: chunking, index config, and whether I need hybrid search.
  • Should I add a cross-encoder re-ranker? Show me the precision-vs-latency tradeoff on my eval set.
Skills

What this agent is good at

  • Design Chunking Strategy
  • Select Embedding Model
  • Configure Vector Index
  • Tune Hybrid Search
  • Run Retrieval Eval