AI / LLM
RAG System Readiness Checklist
A practical readiness checklist for teams planning or reviewing a RAG system before it becomes a production AI feature.
READSERVICE
AI / LLM DEVELOPMENT
AI / LLM DEVELOPMENT
FOCUS
RAG / AGENTS / EVALS
RAG / AGENTS / EVALS
MODEL
PRINCIPAL-LED
PRINCIPAL-LED
OUTPUT
PLAN + BUILD
PLAN + BUILD
AI / LLM DEVELOPMENT
Production-minded AI and LLM systems for real workflows.
Pure Path builds AI features, RAG systems, agentic workflows, local model deployments, and evaluation harnesses with the architecture, observability, and controls needed before teams trust them in production.
CAPABILITIES
Specific help for work that has to survive contact with production.
Our AI work is grounded in product and platform reality: data access, latency, permissions, cost, retrieval quality, testing, and the operational controls that keep AI features useful after launch.
- RAG architecture, retrieval design, chunking, metadata, and source grounding
- Stateful LLM integrations for existing product and internal workflows
- Agentic workflows with tool contracts, approvals, and human-in-loop checkpoints
- Local, offline, and privacy-focused model deployment planning
- Evaluation harnesses for retrieval quality, answer quality, regression, and safety
- Observability, cost monitoring, usage analytics, and production support patterns
PROCESS
The engagement shape depends on the problem. The operating model stays clear.
01
Frame
Define the workflow, risks, data access, permissions, success criteria, and measurable AI value.
02
Prototype
Build a thin vertical slice that tests retrieval, prompts, tools, latency, cost, and user experience before committing to a larger architecture.
03
Evaluate
Create repeatable evals for representative tasks so the team can compare changes, catch regressions, and decide what is ready to ship.
04
Operate
Add logging, observability, feedback loops, cost controls, access boundaries, and support paths so the system can improve safely over time.
RELATED READING
Field notes that go deeper.
AI / LLM
LLM Evaluation Harness: How to Know an AI Feature Is Reliable Enough to Ship
How product and engineering teams can use an LLM evaluation harness to test answer quality, retrieval, regressions, safety, cost, and production readiness.
READCONNECTED PAGES
Useful next pages for this kind of work.
CONNECTED SERVICE
Product Engineering
Connect AI feasibility, architecture, integrations, and delivery planning before an AI prototype becomes a production feature.
SEE PRODUCT ENGINEERINGCONNECTED SERVICE
Data & Growth Analytics
Plan the measurement, usage analytics, dashboards, and data quality checks that make AI systems easier to evaluate after launch.
SEE ANALYTICSFAQ
Common questions before we start.
Can Pure Path help with an existing AI prototype?
Yes. We can review architecture, retrieval quality, prompts, data access, eval coverage, cost, latency, and deployment readiness before the team scales it.
Do you only work with cloud LLMs?
No. We can support cloud APIs, managed AI platforms, local models, offline workflows, and hybrid patterns depending on privacy, cost, latency, and operating needs.
What makes an AI feature production-ready?
The team needs clear success criteria, representative evals, access controls, observability, cost awareness, failure handling, support ownership, and a way to improve the system safely.
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Pure Path works across the systems around the problem.
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