LLM & RAG Design
Design LLM and RAG systems that are grounded, measurable, and production-ready.
For teams building AI assistants, document intelligence, enterprise search, copilots, or workflow automation where retrieval quality and architecture decisions determine the result.
Best fit
Who this is for
This advisory path is designed for teams that need clarity before committing serious engineering budget, vendor contracts, or roadmap direction.
Outputs
What you walk away with
Retrieval architecture across data ingestion, chunking, embeddings, indexes, and ranking
Prompt, context, guardrail, evaluation, and fallback design
Vector database and storage recommendations based on scale and filtering needs
Cost, latency, observability, and quality evaluation plan
Method
How the advisory session works
The work stays practical: clarify context, pressure-test assumptions, choose a direction, and leave with decisions your team can execute.
- 01Review documents, data structure, user tasks, and answer-quality expectations
- 02Define the retrieval and generation flow before selecting tools
- 03Identify failure modes such as stale context, weak metadata, hallucination, and token waste
- 04Produce a practical design your team can implement and test
Questions
Common questions
What makes a RAG system fail?
Most failures come from weak data preparation, poor chunking, missing metadata, shallow evaluation, or treating the LLM as a substitute for system design.
Can this help reduce LLM cost?
Yes. Architecture choices around retrieval filtering, context size, caching, model routing, and evaluation can materially reduce unnecessary token and infrastructure cost.
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