Retrieval-Augmented Generation Prototype
Budget: $25 – $50 USD
I’m building a retrieval-augmented generation (RAG) pipeline and need an experienced engineer who can take it from concept to a working proof-of-value.
Scope
• Connect the generation model to multiple retrieval sources—my internal databases, selected online articles, and live API data.
• Handle data that sits in blob storage and a No-SQL database; you’ll decide the best way to index and chunk both semi-structured and unstructured content.
• Orchestrate the workflow so a single prompt triggers retrieval, relevance ranking, and answer synthesis.
• Ship a functional prototype with clear read-me style documentation outlining how to extend the data connectors and swap in different language models.
Tech I expect you to be comfortable with
• Python and popular LLM frameworks (LangChain, LlamaIndex or similar)
• Vector stores (e.g., FAISS, Pinecone, or an equivalent you recommend)
• Docker or similar containerisation for easy hand-off
• Basic CI for repeatable local builds
Deliverables
1. Well-commented codebase for the RAG pipeline
2. One-click launch instructions (Docker compose or similar)
3. Short report explaining architecture choices and next-step recommendations
I’m available for quick feedback loops and will test the prototype against real internal queries as part of the acceptance criteria.
Scope
• Connect the generation model to multiple retrieval sources—my internal databases, selected online articles, and live API data.
• Handle data that sits in blob storage and a No-SQL database; you’ll decide the best way to index and chunk both semi-structured and unstructured content.
• Orchestrate the workflow so a single prompt triggers retrieval, relevance ranking, and answer synthesis.
• Ship a functional prototype with clear read-me style documentation outlining how to extend the data connectors and swap in different language models.
Tech I expect you to be comfortable with
• Python and popular LLM frameworks (LangChain, LlamaIndex or similar)
• Vector stores (e.g., FAISS, Pinecone, or an equivalent you recommend)
• Docker or similar containerisation for easy hand-off
• Basic CI for repeatable local builds
Deliverables
1. Well-commented codebase for the RAG pipeline
2. One-click launch instructions (Docker compose or similar)
3. Short report explaining architecture choices and next-step recommendations
I’m available for quick feedback loops and will test the prototype against real internal queries as part of the acceptance criteria.