Towards Data Science
Jul 19
Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval
Enterprise Document Intelligence [Vol. 1 #6quinquies] - Prompt engineering, then context engineering, then loop engineering.
RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop
Enterprise Document Intelligence [Vol. 1 #13] - Putting the patterns together, and why this is what “agentic RAG” should look like The post RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop appeared first on Towards Data Science .
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Towards Data Science
2d ago
Loop Engineering for RAG: The Small Loops Inside Each Step, the Big Loops Across the Pipeline
Enterprise Document Intelligence [Vol. 1 #13bis] - The four bricks return useful results most of the time.
Towards Data Science
Aug 3
Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On
Enterprise Document Intelligence [Vol. 1 #M2] - Every RAG system is built in three engineering layers stacked on one LLM call: prompt (the call itself), context (what fills the model’s window), loop (when the next call fires and when it stops).
Towards Data Science
Aug 6
Loop Engineering for Cross-References: When RAG Answers ‘see Section 7.2’ Instead of the Actual Answer
Enterprise Document Intelligence [Vol. 1 #11] - When the first answer points elsewhere in the document, the pipeline loops back to fetch the linked context The post Loop Engineering for Cross-References: When RAG Answers ‘see Section 7.
Towards Data Science
Aug 7
Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One
Enterprise Document Intelligence [Vol. 1 #12] - The category of question most RAG pipelines silently fail on, and the pipeline shape that handles them The post Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One appeared first on Towards Data Science .
Towards Data Science
Jul 22
Loop Engineering for RAG Generation: Iterate top-k One at a Time
Enterprise Document Intelligence [Vol. 1 #8bis] - Two regimes for sending retrieved candidates to the generation brick, the sufficiency signal that picks between them, and the per-question type dispatch that makes it cheap The post Loop Engineering for RAG Generation: Iterate top-k One at a Time appeared first on Towards Data Science .
Towards Data Science
Jul 17
A Production RAG Pipeline in Action: Every Answer Typed and Cited
Enterprise Document Intelligence [Vol. 1 #9B] - One call wires the four upgraded bricks together, run on a paper, a NIST standard, and a report with a broken TOC The post A Production RAG Pipeline in Action: Every Answer Typed and Cited appeared first on Towards Data Science .
Towards Data Science
Jul 3
AI Agents Explained: What Is a ReAct Loop and How Does It Work?
How agents reason, act, and observe their way to a final answer, one step at a time The post AI Agents Explained: What Is a ReAct Loop and How Does It Work? appeared first on Towards Data Science .
Towards Data Science
Jun 18
Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit
Enterprise Document Intelligence [Vol. 1 #6c] - The decisions the parser makes on top of the user string, using the document’s profile: dispatch, activations, full schema, three approaches to deciding what fires, the audit _meta block, and a broker-corpus walkthrough The post Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit appeared first on Towards Data Science .
Towards Data Science
Jun 24
Why I Stopped Using One Agent and Built a Multi-Agent Pipeline Instead
A practical walkthrough using text-to-SQL as the example The post Why I Stopped Using One Agent and Built a Multi-Agent Pipeline Instead appeared first on Towards Data Science .
Towards Data Science
Jul 4
Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination
Enterprise Document Intelligence [Vol. 1 #8A] - The schema is the contract: every field is a question the pipeline asks the model, and every answer is checkable The post Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination appeared first on Towards Data Science .
Towards Data Science
Jul 17
One RAG Pipeline, Four Very Different PDFs: Same Four Bricks, Every Answer Typed and Cited
Enterprise Document Intelligence [Vol. 1 #9B] - One call wires the four upgraded bricks together, run on a paper, a NIST standard, and a report with a broken TOC The post One RAG Pipeline, Four Very Different PDFs: Same Four Bricks, Every Answer Typed and Cited appeared first on Towards Data Science .