RAG Architecture & Integration
I help teams evaluate and implement practical retrieval-augmented generation (RAG) architectures, including retrieval pipelines, grounding strategies, operational tradeoffs, and integration into existing engineering systems.
The Approach
What I Address
Teams adopting RAG without a clear architecture face unreliable answers, fragile retrieval pipelines, unclear ownership, and no practical way to measure whether the system is improving.
Without evaluation of use cases, integration points, and operational tradeoffs, RAG becomes a black box that engineering teams cannot trust or evolve confidently.
How I Work
I provide RAG architecture and integration guidance focused on production engineering:
- Assess product and workflow use cases, data sources, and quality expectations
- Evaluate retrieval pipeline options, grounding strategies, and integration boundaries
- Define operational tradeoffs across cost, latency, maintainability, and team capability
The Value
Outcomes
- Clear RAG architecture aligned with product goals and engineering constraints
- Retrieval and grounding approach that fits your content, queries, and team skills
- Defined quality signals and iteration path for ongoing improvement
- More maintainable integration into existing products and workflows
- Confidence in adopting RAG without overbuilding or chasing hype
Deliverables
- RAG architecture assessment and integration recommendations
- Retrieval pipeline, grounding, and quality measurement guidance
- Operational tradeoff analysis and ownership model recommendations
- Phased implementation roadmap with engineering integration points
- Follow-up consultation on rollout and iteration
The Fit
Who This Is For
Teams evaluating or improving knowledge-base and document Q&A capabilities, engineering organizations adding grounded search to products, and internal teams improving support or documentation workflows.
Ideal timing includes when scoping a first RAG initiative, during redesign of underperforming retrieval setups, when scaling content volume, or when establishing practical quality benchmarks.
Why RAG Architecture Matters
RAG can improve grounded answers in products and internal tools, but only when retrieval design, quality measurement, and operational ownership are treated as engineering concerns. Without that discipline, teams ship fragile pipelines that are hard to maintain and expensive to fix later.