The technical foundation for the working AI practitioner.
By 2026, prompt engineering is a profession, AI engineering is a discipline, and context engineering is
a recognized craft. The AI Engineering Bible, Volume I is the technical reference for the working
professional responsible for AI output that has to hold up under load.
Twenty-seven chapters: what models actually are and how they work, the mental models needed to predict
their behavior, the durable techniques that survive model upgrades, and the failure modes that bite teams
who skipped the foundation.
Inside:
-
- The mental models - what a transformer actually is, how attention works in production frontier
models, and where the abstraction breaks down.-
- The clarity discipline - the rules that govern good prompts across every model, recallable at
the moment you are writing.-
- Examples and few-shot geometry - when in-context examples help, when they hurt, and how to
place them well.-
- Chain-of-thought and reasoning models - patterns that hold up across Claude reasoning, GPT-5
o-series, and Gemini deep-think modes.-
- Structured output - JSON mode, schemas, XML tags, prefilling, stop sequences.
- The multimodal stack - images, audio, video, PDFs.-
- Long-context strategies - what works at 200K, 1M, and 2M tokens; how to anchor against
hallucination at depth.-
- Iteration and meta-prompting - disciplines for improving your own practice.-
Who this is for:
The serious practitioner - the engineer, technical PM, consultant, analyst, or operator now responsible
for AI work who wants to be unambiguously good at it. No machine-learning background required. A
willingness to be precise and to test your assumptions is.
The series:
Volume I is the craft of prompting. Volume II is production engineering - system design, evals,
observability, cost, security, RAG, agents. Volume III is the corpus - 2,160 prompts by use case,
industry, and pattern. Each volume stands alone.
Val Scancella writes the working professional's reference for AI craft.