Architecture
RAG Architecture Design
We design retrieval-augmented generation architectures that ground LLMs in your enterprise knowledge. From data pipelines to evaluation frameworks.
Architecture components
- Data ingestion pipelines for documents, databases, and APIs.
- Chunking and embedding strategies optimised for your domain.
- Vector database selection and indexing architecture.
- Retrieval orchestration with reranking and hybrid search.
Deliverables
- Reference architecture documentation and diagrams.
- Technology selection with trade-off analysis.
- Evaluation framework for retrieval and generation quality.
- Implementation roadmap with phased delivery.
The RAG stack
- Data layer: Ingestion, transformation, and metadata extraction.
- Embedding layer: Model selection, chunking, and vectorisation.
- Retrieval layer: Vector search, hybrid retrieval, and reranking.
- Generation layer: Prompt engineering, context assembly, and LLM orchestration.
- Evaluation layer: Quality metrics, feedback loops, and monitoring.
Who this is for
- Enterprises building knowledge assistants and search systems.
- Product teams launching AI features grounded in company data.
- Engineering leads designing scalable retrieval infrastructure.
- Organisations with complex document corpora and knowledge bases.
Why RAG architecture matters
Large language models hallucinate. They generate plausible-sounding content that may be factually wrong. Retrieval-augmented generation solves this by grounding model outputs in your actual data.
But RAG is not a simple integration. Poor chunking destroys context. Wrong embedding models miss semantic nuance. Naive retrieval returns irrelevant results. Without proper architecture, RAG systems fail silently, delivering confident answers that are simply wrong.
We design RAG architectures that work. Retrieval that finds the right context. Generation that uses it correctly. Evaluation that catches failures before users do.
Technical considerations
Chunking strategy
Document structure, semantic boundaries, and retrieval patterns determine optimal chunk sizes. We analyse your content to design chunking that preserves meaning.
Embedding selection
General-purpose embeddings often underperform on domain-specific content. We evaluate embedding models against your data and use cases.
Hybrid retrieval
Vector search alone misses keyword matches. BM25 alone misses semantic similarity. We design hybrid retrieval with fusion strategies tuned to your needs.
Evaluation framework
Retrieval precision, context relevance, answer faithfulness. We build evaluation pipelines that measure what matters and catch regressions early.
RAG architecture FAQ
Which vector database should we use?
It depends on scale, latency requirements, and existing infrastructure. We evaluate options against your specific constraints.
How do you handle multi-modal content?
We design pipelines for text, tables, images, and structured data with appropriate extraction and embedding strategies for each.
What about security and access control?
We design retrieval with document-level permissions, ensuring users only access content they are authorised to see.
How do you measure RAG quality?
We implement evaluation frameworks measuring retrieval relevance, context utilisation, answer faithfulness, and end-to-end task success.