Frequently Asked Questions
Common Questions About RAG Pipelines and Context Engineering
Everything you need to know before registering.
How does RAG fit into the context engineering architecture? +
RAG fits into the context engineering architecture as the knowledge retrieval layer within the semantic blueprint pipeline. When the context engine constructs a semantic blueprint for an agent, it calls the RAG pipeline to retrieve relevant knowledge that is injected into the blueprint as cited context. The agent then works with explicitly grounded knowledge rather than relying on its training parameters for factual claims.
What is the relationship between semantic blueprints and RAG retrievals? +
Semantic blueprints define the structure of an agent's context, including a designated knowledge section populated by RAG retrievals. The blueprint specifies what knowledge domain to query, how much context window space to allocate to retrieved content, and how retrieved content should be attributed in the agent's response. This tight integration between blueprint design and RAG query formulation is what makes context-engineered RAG reliable.
How does context engineering improve RAG accuracy? +
Context engineering improves RAG accuracy in several ways: semantic blueprints provide structured query context that improves retrieval relevance, explicit knowledge boundary definitions reduce queries for which RAG has no reliable answer, and the Glass-Box observability layer captures retrieval quality data that enables systematic improvement of the RAG configuration.
How does the Glass-Box architecture make RAG pipelines observable? +
The Glass-Box architecture logs every RAG operation: the original query, the reformulated retrieval query, retrieved document IDs and scores, selected chunks and their metadata, citation extraction results, and validation outcomes. This comprehensive logging makes RAG behavior fully observable so you can see exactly what was retrieved for any query and how citations were used in the generated response.
What happens in the context engine when RAG retrieval confidence is low? +
When RAG retrieval confidence is below the threshold, the context engine routes the request through an alternative path rather than proceeding with low-quality knowledge grounding. Options include: escalating to a more specialised agent with better knowledge coverage, returning an explicit uncertainty response to the user, requesting human review for queries that fall outside the knowledge base, or attempting query reformulation for a second retrieval attempt.
How do I tune the interaction between context engineering and RAG for my specific use case? +
Tuning the context engineering and RAG integration involves adjusting retrieval confidence thresholds based on your acceptable hallucination rate, tuning the knowledge allocation in semantic blueprints based on how knowledge-intensive your tasks are, calibrating the re-ranking model for your document domain, and configuring the citation validation strictness based on your accuracy requirements. The Glass-Box data provides the metrics needed to make all of these tuning decisions systematically.
