Markdown source

Beyond RAG: Build a Relational Context Engine from Scratch

Conference Context

Session Description

In this workshop we'll explore the importance of context engines in modern engineering workflows, and we'll look at why traditional RAG techniques are no longer enough to deliver the context agents need. We'll build a structured query engine that fills the gaps left by RAG, translating natural language into validated database queries over GitHub PR and Issue data. We'll implement schema-aware prompting, identity resolution, query validation, and error-driven retry loops, and you'll walk away with a working query engine for your GitHub repository.

Media Evidence

Mergeable by default: Building the context engine to save time and tokens — Peter Werry, Unblocked (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

Evidence Graph

This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

Media Signals

Agent Reading Notes

Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

Transcript Status

Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Not fetched yet.

People

Supporting Slides

Slide Evidence

Synthesis

Synthesized Breakdown

Beyond RAG: Build a Relational Context Engine from Scratch ## Conference Context - Date/time: 2026-06-29 · 12:10pm-1:10pm - Track/room: Workshops Day 1 · Track 7 - Speaker(s): Peter Werry - Session type/status: workshop · confirmed - Track: Workshops Day 1 - Room: Track 7 - Session type: workshop - Status: confirmed ## Session Description In this workshop we'll explore the importance of context engines in modern engineering workflows, and we'll look at why traditional RAG techniques are no longer enough to deliver the context agents need. We'll build a structured query engine that fills the gaps left by RAG, translating natural language into validated database queries over GitHub PR and Issue data. We'll implement schema-aware prompting, identity resolution, query validation, and error-driven retry loops, and you'll walk away with a working query engine for your GitHub repository. ## Media Evidence Mergeable by default: Building the context engine to save time and tokens — Peter Werry, Unblocked (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

Speaker And Company Context

Topics Covered

Derived Links And Source Material

Novel Concepts / Clever Methods

Evidence Boundary

This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.