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Quick Intro~7 MIN· GENAI

Generative AI & foundation models

Full Study

A scannable trailer of the 11-lesson course. Read top to bottom — no clicks needed.

INTROBLOCK · 01
GENAI · 7 MIN PREVIEW

Foundation models for engineers who ship.

53% population adoption in 3 years. The math is the same. The deployment is what changes.

CONCEPTBLOCK · 02

The one-line difference

A foundation model is a single pretrained network you adapt to many tasks via prompting, retrieval, or fine-tuning. You don't train it — you choose it, parameterise it, and budget its tokens. Picking the right model and the right access pattern matters more than any prompt trick.
DIAGRAMBLOCK · 03

Three ways to bend a foundation model

cheapestfresh dataexpensiveFOUNDATIONPROMPTRETRIEVETUNE
Try in this order. Most teams stop at retrieve.
CODEBLOCK · 04

Foundation model in 10 lines

PYTHON
1from openai import OpenAI
2
3client = OpenAI()
4
5resp = client.chat.completions.create(
6 model="gpt-4o-mini",
7 messages=[
8 {"role": "system", "content": "You are a senior backend engineer. Be terse."},
9 {"role": "user", "content": "What's the latency cost of a 4k-token prompt?"},
10 ],
11 max_tokens=200,
12)
13print(resp.choices[0].message.content)
CHEATSHEETBLOCK · 05

Remember when shipping

01Pick the smallest model that hits your eval bar.
02Token cost dominates total spend at scale — measure both directions.
03Temperature is not magic. Start at 0 for deterministic tasks.
04Set max_tokens. Always. It's the only hard cost cap.
05Cache prompts you reuse. Cache hits are 5-10× cheaper.
MINIGAME · RAPIDFIRETFBLOCK · 06
Bigger foundation models always produce better answers.
CLAIM 1/5 · READY · scroll into view
LESSON COMPLETEBLOCK · 07

That's the trailer.

NEXTFoundation models 101
WHAT YOU'LL WALK AWAY WITH

Real skills, real career delta.

Skills you'll gain

12
  • Model selection & routingProduction

    Pick frontier vs mini vs reasoning vs local models against latency, cost, and quality budgets — and route requests between them in one service.

  • Cost-bounded LLM featuresProduction

    Ship features with hard token budgets, max_tokens caps, and per-request $ tracking — defendable on a finance review.

  • Prompt engineeringWorking

    Author and maintain prompts that survive 3+ revisions: zero-shot, few-shot, CoT, structured-output, role design, anti-drift patterns.

  • Structured outputProduction

    Build JSON-mode + Pydantic + Instructor services that validate on every turn and retry on schema failure.

  • Function calling & tool useWorking

    Wire single and parallel tool-use, design idempotent tool contracts, and decide when an agent is the wrong answer.

  • Eval-driven LLM developmentProduction

    Write Promptfoo / DeepEval suites and gate releases on regression — turn prompts into testable, versioned artifacts.

  • Streaming & latency engineeringWorking

    Implement chunked SSE, partial-JSON streaming, and cut perceived chat latency from 3s+ to under 500ms.

  • Caching (prompt + semantic)Production

    Design cacheable prefixes, set up prompt caching and Redis-backed semantic cache — verified 40-70% spend reduction.

  • LLM observabilityProduction

    Stand up LiteLLM + Prometheus + Grafana + Loki to trace every call with prompt hash, tokens, cost, and provider id.

  • Safety & prompt-injection defenceWorking

    Apply NeMo Guardrails / Llama Guard, run a red-team drill on your own service, and ship a hardened input/output filter chain.

  • Local-first deploymentWorking

    Run Ollama / vLLM with small models (Phi-4, Llama-3.2) and route to hosted APIs only on overflow — works offline, beats compliance reviews.

  • When NOT to generateProduction

    Replace LLM calls with regex / SQL / classifiers / embeddings where deterministic — shown to cut spend 30%+ on real audits.

Career & income delta

Career moves
  • Apply for and credibly interview for AI / GenAI / LLM Engineer roles
  • Add 'shipped a cost-bounded LLM feature' to your resume with provable artifacts
  • Lead an LLM rollout at your current company instead of waiting for one to land
  • Move from frontend / backend generalist into the AI-platform sub-track at your org
  • Sell freelance / contract LLM-integration projects with a portfolio of ten Docker images
Income impact
  • GenAI Engineer roles in 2026 pay 18-35% above equivalent backend roles in the same metro (Levels.fyi snapshot)
  • Contract LLM-integration work commands $120-220 / hr in NA / EU — proof artifacts close the gap fast
  • Internal moves at large companies typically come with a level + 12-22% comp lift
  • A single 'cut LLM bill 60%' war story on your CV moves you up a band on a senior interview loop
Market resilience
  • GenAI engineering postings are up 4.1× YoY (LinkedIn Workforce Report 2026 Q1)
  • Roles automated by AI shrink 12% YoY; roles that BUILD WITH AI grow 28% YoY
  • Local-first / SLM skills protect against provider-pricing shocks
  • Eval + observability skills age slower than any specific model — they survive every model migration