AI Engineering in 76 Minutes – A Quick‑Start Guide

Based on the book “AI Engineering” by Chip Win and distilled into a single, high‑level overview.

1. Why AI Engineering Matters

Reason	Impact
Foundation models have become powerful and accessible	$300k+ salaries, rapid job growth
Barriers dropped – no need to train from scratch	Focus on adaptation (prompting, RAG, fine‑tuning)
Fastest‑growing discipline	Companies are racing to build production‑ready AI systems

Takeaway: AI Engineering is about building with existing large models, not creating them.

2. Foundation Models 101

Self‑supervised training – learn by predicting missing parts of data (no manual labels).
Large Language Models (LLMs) evolved from text‑only to multimodal (image, video).
Typical architecture: Transformer with attention.
Key concepts:
- Queries, Keys, Values → attention scores.
- Multi‑head attention lets the model focus on different token groups.
- Context window limits how much text can be fed in one go.

Why it matters: Knowing the architecture helps you understand why a model behaves the way it does (e.g., hallucinations, token limits).

3. Prompt Engineering – The First Line of Adaptation

Component	What it is	Tips
Task description	Role & expected output	Be explicit (e.g., “You are a medical assistant”)
Examples (shots)	Show how to do it	Few‑shot works best; keep them short
Concrete task	The actual user query	Keep it separate from instructions
System vs. User prompts	System: role; User: query	Follow the model’s chat template exactly
Output format	JSON, Markdown, plain text	Specify to avoid preambles

Prompt‑Engineering Checklist

Clear instructions
Persona adoption
Representative examples
Output format defined
Complex tasks → split into subtasks
Chain‑of‑Thought or self‑critique for reasoning
Version control & experiment tracking

Bottom line: A well‑crafted prompt can unlock most of a model’s potential before you even touch code.

4. Retrieval Augmented Generation (RAG)

Stage	What it does
Retriever	Finds relevant documents/chunks
Generator	Uses retrieved info to answer

Retrieval Strategies

Method	How it works	Pros	Cons
Term‑based (TF‑IDF)	Keyword matching	Fast, low cost	Misses semantics
Embedding‑based	Vector similarity	Semantically richer	More compute, expensive
Hybrid	Combine both	Balance speed & accuracy	Adds complexity

Practical Tips

Chunking – equal‑size, overlapping chunks; experiment with size & overlap.
Re‑ranking – apply recency or domain relevance.
Query rewriting – add context or expand synonyms.
Multi‑modal RAG – images, tables, SQL queries.

Result: RAG lets you give a model fresh data without retraining.

5. Agents – Going Beyond Passive Retrieval

Tool Type	Example	Use‑case
Knowledge‑augmentation	RAG, SQL executor	Pull data from DB
Capability‑extension	Calculator, code interpreter	Perform math, run code
Write‑action	Email API, order system	Trigger external actions

Agent Workflow

Plan – model decides steps.
Validate – check plan against constraints.
Execute – call tools.
Iterate – refine plan if needed.

Safety & Reliability

Guardrails – input/output filtering, toxicity checks.
Human‑in‑the‑loop for high‑stakes tasks.
Evaluation – plan validity, tool usage, success rate.

Key insight: Agents are highly powerful but also high‑risk; careful orchestration is essential.

6. Fine‑Tuning – When Prompting Isn’t Enough

Approach	What it changes	Typical use‑case
Full fine‑tuning	All weights	Domain‑specific performance
Parameter‑Efficient Tuning (PET)	Small adapters or prompt tokens	Limited compute, few data
Adapter (LoRA)	Low‑rank updates	Keeps inference fast
Soft‑prompt	Trainable tokens	Simple, low overhead

When to Fine‑Tune

You need structured outputs (e.g., tables, JSON).
The model’s behavior is wrong (hallucinations, style).
You have domain data that is scarce but crucial.

Data Requirements

Fine‑Tuning Type	Data Volume	Example
Full	Thousands–millions	Medical records
PET (LoRA)	Hundreds–thousands	Legal summaries
Soft‑prompt	Tens–hundreds	Instruction–response pairs

Rule of thumb: Start with PET; only move to full fine‑tune if you hit a ceiling.

7. Evaluation – The Hardest Part

Metric	What it measures	When to use
Cross‑entropy / Perplexity	Token prediction quality	Training diagnostics
Exact match	Binary correctness	Closed‑domain Q&A
Lexical similarity	Token overlap	When references exist
Semantic similarity	Meaning equivalence	Open‑domain tasks
AI judge	Human‑like scoring	Scale‑up without humans
Functional correctness	Task success	Booking, code execution

Building an Evaluation Pipeline

Define business metrics (e.g., % factual consistency).
Create a rubric – clear, unambiguous.
Run automated tests – AI judges, reference comparisons.
Add human spot‑checks – sanity, edge cases.
Measure bias & safety – toxicity, self‑bias.

Remember: Evaluation is iterative – keep refining the rubric as the model evolves.

8. Inference Optimization – Making It Fast & Cheap

Bottlenecks

Compute‑bound – heavy matrix ops (image generation).
Memory‑bandwidth‑bound – token generation in LLMs.

Model‑Level Techniques

Technique	What it does	Typical benefit
Quantization	Reduce bit‑width	2–4× speed, lower RAM
Pruning	Remove low‑importance weights	Smaller model
Distillation	Train smaller mimic	Faster inference
Speculative decoding	Draft + verify	Speed up token generation
Parallel decoding	Generate multiple tokens	Reduce sequential bottleneck

Service‑Level Techniques

Technique	How it works	Impact
Batching	Process many requests together	↑ throughput
Decoupled pre‑fill & decode	Separate stages	Reduce contention
Prompt caching	Store common prefixes	Save compute
Replica parallelism	Multiple model copies	Low latency
Model parallelism	Split model across GPUs	Scale larger models

Takeaway: Start with quantization + batching; then layer on more advanced tricks as needed.

9. End‑to‑End AI Application Architecture

Base layer – direct model call (API or self‑hosted).
Context construction – RAG, document upload, tool integration.
Guardrails – input/output filtering, safety checks.
Model routing – intent classifier → appropriate model or pipeline.
Caching – KV cache, prompt cache for repeated patterns.
Complex logic – agents, multi‑step reasoning, write actions.
Observability – logs, metrics (MTTD, MTTR, CFR).
Feedback loop – explicit/implicit user signals to improve data & models.

Design principle: Add only what solves a real problem; keep the stack lean until complexity is justified.

10. The Power of User Feedback

Type	Example	How to capture
Explicit	Thumbs‑up/down, star rating	UI prompts, post‑interaction surveys
Implicit	Early termination, repeated clarifications	Session logs, interaction duration

Best practices

Ask for feedback strategically (e.g., after a mistake, at natural checkpoints).
Use feedback to auto‑label data for fine‑tuning or to trigger human review.
Treat feedback as proprietary data – it gives you a competitive edge.

11. Quick Summary

Topic	Key Point
Foundation Models	Transformer‑based, self‑supervised, large context windows
Prompt Engineering	Clear instructions + examples + output format
RAG	Retriever + generator; embedding‑based retrieval is best
Agents	Plan → validate → execute; guardrails essential
Fine‑Tuning	PET first; LoRA or soft‑prompts for efficiency
Evaluation	Mix automated metrics with human sanity checks
Inference	Quantize + batch; cache prompts; use replica parallelism
Architecture	Start simple, layer RAG, guardrails, routing, caching, observability
Feedback	Explicit + implicit; turn into data for continuous improvement

Final thought: AI Engineering is not just about training a big model—it’s about designing a system that adapts, evaluates, optimizes, and improves continuously. Use the book as your deep dive, but let this guide help you map the high‑level terrain before you start building.