Learning track 02
Prompting & Adaptation
Move from hand-written instructions to context systems, fine-tuning, and programmatic prompt optimization.
- Guides
- 3
- Sections
- 23
- Cards
- 103
Suggested sequence
Follow the track or enter where the problem begins.
Every guide is self-contained. The sequence only preserves prerequisite order when one topic depends on another.
- 01Vol. 04
Fine-Tuning Strategies
When to use fine-tuning, and when is RAG or prompting enough? LoRA, QLoRA, SFT, DPO, RLHF, distillation - broken down into focused cards. How to prepare data, how to evaluate the result, how not to waste the GPU.
- Sections
- 8
- Cards
- 27
- 02Vol. 28
Context Engineering & Prompt Caching
This volume is not about the “magical prompt”, but about the engineering layer around it: from what blocks the context is collected, how the state lives between moves, what exactly is cached, when compaction is needed, how not to drown in tool outputs and what to measure in production. The idea is simple: the quality of an LLM system is often determined not by the model per se, but by the context you give it and how you manage its life cycle.
- Sections
- 7
- Cards
- 35
- 03Vol. 34
DSPy & GEPA: Implementation Cookbook
A big volume about how to really build systems on DSPy: all the key abstractions from official documentation, a built-in module map, data design and metrics, the choice of an optimizer, deep dive by GEPA and step-by-step rollout plans from the first baseline to production.
- Sections
- 8
- Cards
- 41