From first prompt to agents in production

29 full lessons you read and build here, in three levels. Each level ends in a system a Zambian business would actually run: a ZRA tax assistant, a production labour-compliance assistant, and a back-office agent that reconciles mobile money. Marked against a rubric, not a certificate for turning up.

29 lessons · 3 capstones · 3 lessons open to everyone · K850 for the rest, with weekly build sessions

Codes come with the K850 practice. Book a place

What is in every lesson

  • The full lesson, here

    Problem, concepts, diagrams and a step-by-step build, written for engineers. No jumping off to other sites.

  • Working code

    Every build step with highlighted code, the complete source files and a starter app to extend.

  • Quizzes that teach

    A warm-up before you read and a check after, each answer explained.

  • A tutor that stays on topic

    Ask about the lesson; Jev scores how closely each answer sticks to the lesson text.

  • A capstone per level

    Each lesson adds one milestone to a real Zambian system, marked against a weighted rubric.

  • Reviews and interview practice

    Paste your milestone evidence for a check-by-check review; practise real interview questions.

Not sure where to start?

Answer two questions and say what you want to build. Jev picks your level and shows how sure it is; DeepSeek turns the first lesson into a project brief for your idea.

How much Python have you written?
Have you used an AI model from code?

The path

Intermediate. Take it to production

Evaluation before tuning, guardrails, cost in kwacha, gateways, observability and staged rollout.

You finished the beginner level or built a RAG feature before. Expect 6 to 10 hours per lesson including the milestone.

  1. I1Advanced RAG (Chunking, Reranking, Hybrid Search)Hybrid retrieval with reranking and role filters 90 minutes8 sections · 5 quiz questionsOpen
  2. I2Evaluation & Testing LLM ApplicationsThe golden set comes before tuning 45 minutes8 sections · 5 quiz questionsLocked
  3. I3Guardrails, Safety & Content FilteringGuards on the way in and the way out 45 minutes8 sections · 5 quiz questionsLocked
  4. I4Caching, Rate Limiting & Cost OptimizationKnow the cost of every answer in kwacha 45 minutes8 sections · 5 quiz questionsLocked
  5. I5Prompt Caching and Context CachingDesign for a high cache hit rate 60 minutes8 sections · 5 quiz questionsLocked
  6. I6Building a Production LLM ApplicationShip it as a service 120 minutes8 sections · 5 quiz questionsLocked
  7. I7LLM Observability Stack SelectionSee every request 60 minutes7 sections · 6 quiz questionsLocked
  8. I8AI Gateways — LiteLLM, Portkey, Kong AI Gateway, BifrostPut a gateway in front of the models 60 minutes7 sections · 6 quiz questionsLocked
  9. I9Shadow Traffic, Canary Rollout, and Progressive Deployment for LLMsRelease without betting the business 60 minutes7 sections · 6 quiz questionsLocked
  10. I10A production labour-compliance assistantAn HR and labour-law assistant for Zambian employers, built to the production standard used for regulated-domain RAG: golden set, red team, role-based access, cost per query in kwacha, drift monitoring and a staged rollout. 30 to 40 hoursCapstone · weighted rubric · data packBrief open

Advanced. Engineer agents you can trust

Tool contracts, MCP, the agent loop, state and memory, orchestration, failure modes, injection defence, evaluation and runtime control.

You finished the intermediate level or run LLM features in production. Expect 8 to 12 hours per lesson; the data pack gives you realistic statements to work on.

  1. A1The Tool Interface — Why Agents Need Structured I/ODraw the line between reading and acting 45 minutes7 sectionsOpen
  2. A2Tool Schema Design — Naming, Descriptions, Parameter ConstraintsSchemas a model can use correctly 45 minutes7 sectionsLocked
  3. A3MCP Fundamentals: Stateless Requests and JSON-RPCServe the tools over MCP 55 minutes7 sections · 6 quiz questionsLocked
  4. A4The Agent Loop: Observe, Think, ActA loop that always stops 60 minutes8 sections · 7 quiz questionsLocked
  5. A5Tool Use and Function CallingTool errors the agent can recover from 60 minutes8 sections · 7 quiz questionsLocked
  6. A6Agent Memory — Virtual Context and Memory PagingRemember customers, forget carefully 75 minutes8 sections · 7 quiz questionsLocked
  7. A7Stateful Graph Orchestration — Durable Execution and CheckpointsMake the workflow a state graph 75 minutes8 sections · 7 quiz questionsLocked
  8. A8Orchestration Patterns: Supervisor, Swarm, HierarchicalChoose the orchestration, justify it 60 minutes8 sections · 7 quiz questionsLocked
  9. A9Failure Modes: Why Agents BreakBreak it on purpose 60 minutes8 sections · 7 quiz questionsLocked
  10. A10Prompt Injection and the PVE DefenseInstructions hidden in payment references 75 minutes8 sections · 7 quiz questionsLocked
  11. A11Eval-Driven Agent DevelopmentGrade trajectories, not just answers 60 minutes8 sections · 7 quiz questionsLocked
  12. A12Production Runtimes: Queue, Event, CronRun it for a month 60 minutes8 sections · 7 quiz questionsLocked
  13. A13Agent Observability: Langfuse, Phoenix, OpikOperate it from the traces 45 minutes8 sections · 7 quiz questionsLocked
  14. A14An observable back-office agentA finance back-office agent for a Zambian SME that reconciles mobile-money statements against invoices, prepares Smart Invoice entries for review and chases overdue customers on WhatsApp, with every money-affecting action approved by a person and every step traceable. 40 to 50 hoursCapstone · weighted rubric · data packBrief open

Learn it with people, not alone

The lessons are the path. The K850 practice is the group that walks it with you: a small group, weekly build sessions and code review, in Lusaka and remote, and a certificate with a project repo you can show employers.

Built on open work

The lesson text, code and quizzes are adapted from open-source courses, credited below under their licences. The capstones, milestones, data packs, reviews and Zambian context are ours.

  • AI Engineering from ScratchRohit GhumareMIT · lesson text, code and quizzes
  • Awesome LLM AppsShubham SabooApache-2.0 · starter code
  • AI Engineering Interview QuestionsOutcome SchoolApache-2.0 · interview questions
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