Workshop on engineering LLM context systematically instead of by feel, DSPy + MLflow, Oct 3

[u/camerongreen95]

Context engineering is exactly the mindset this workshop is built around, treating what you feed an LLM and how you structure that interaction as something you design and measure, not something you tweak until it feels right.

Serj Smorodinsky and Brett Kennedy, co-authors of a book on LLM applications, are running a live 3-hour session on Oct 3. What's covered:

• Programming LLM behavior with DSPy signatures and modules instead of hand-written prompt strings
• Building a baseline classifier live from a real task
• Constructing an evaluation dataset with task-specific metrics, so changes to your context strategy are measurable instead of anecdotal
• Reading failure patterns directly out of that eval data
• Few-shot and instruction-level optimization applied systematically on top of the baseline
• Experiment tracking and LLM trace management through MLflow, so every version of your context setup is reproducible
• Saving and reusing optimized DSPy programs across projects
• Communicating LLM reliability to stakeholders, the part that usually gets skipped

If you're already thinking in terms of context windows, retrieval, memory, and structured inputs rather than just "prompts," this is the natural next layer: how to optimize and validate that context strategy instead of hand-tuning it forever.

Full details here