Understand the generation step of RAG: how LLMs use augmented context to produce grounded responses, how to enforce structured outputs with Py...
Understand the augmentation step of RAG: how retrieved documents are structured into prompts, how metadata and citations improve response qual...
Understand the retrieval step of RAG: Learn how database queries, keyword search, vector search, and hybrid approaches find the right informat...
The Corvus.Json.CodeGeneration library analyses JSON Schema and builds a TypeDeclaration tree that maps schema patterns to code patterns. The ...
The final post in the Optimising DAX series: the CALCULATE trap, variables and IF.EAGER, slicer costs, and a practical approach to isolating s...
Data materialisation is when the storage engine gives up on efficient processing and rebuilds the entire table. This post explains what trigge...
A deep dive into the cross-assembly linking, PDB-based source links, TFM scanning, enrichment merging, and search indexing that power our API ...
We generate about 8,800 API reference pages from 16 libraries across two engine versions, with source links, TFM availability badges, and hand...
A flat copilot-instructions.md works for small projects. When your codebase hits 500k lines, 19 projects, and multiple code generators, you ne...
When you execute a DAX query, two engines divide up the work. Understanding the split between the formula engine and the storage engine is key...
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