AI-integrated development

• How LLMs WorkPractice

  0/16 lessons

  1. Tokens and next-word prediction50 min
  2. The context window50 min
  3. Sampling and temperature50 min
  4. What models cannot do50 min
  5. Hallucination and trust55 min
  6. Module assignment: Helpdesk guard55 min
  7. Byte pair encoding by hand40 min
  8. Why the same text costs different tokens by language35 min
  9. Logits and probabilities40 min
  10. Three training stages40 min
  11. Attention in plain words45 min
  12. Word order and position40 min
  13. Token embeddings inside the model45 min
  14. Perplexity40 min
  15. Stop sequences and end-of-text40 min
  16. Parameters, memory and quality45 min

  Module quiz

• LLM APIsPractice

  0/18 lessons

  1. Your first chat API call50 min
  2. Streaming responses50 min
  3. API keys and secrets50 min
  4. Cost, rate limits and errors55 min
  5. Structured output and JSON55 min
  6. Module assignment: Safe ticket triage service55 min
  7. Choosing a model for a task40 min
  8. Request settings and stop reasons50 min
  9. Images and files as input40 min
  10. Timeouts and cancellation40 min
  11. Record-and-replay tests40 min
  12. Background jobs for slow calls45 min
  13. Batch requests for offline work35 min
  14. Pinning model versions and deprecations35 min
  15. A thin adapter for providers45 min
  16. The same calls in Python40 min
  17. Provider content filters and refusals35 min
  18. Stateless calls and rebuilding history35 min

  Module quiz

• Prompt EngineeringPractice

  0/19 lessons

  1. Roles and system prompts50 min
  2. Clear instructions and structure50 min
  3. Showing examples (few-shot prompting)50 min
  4. Reasoning and chain of thought55 min
  5. Prompt injection basics55 min
  6. Module assignment: Ticket triage prompt lab55 min
  7. Prompt chaining45 min
  8. Self-consistency40 min
  9. Self-critique and revision45 min
  10. Least-to-most prompting45 min
  11. Positive versus negative instructions40 min
  12. Delimiters and sections40 min
  13. Prompt templates with variables40 min
  14. Versioning prompts in files45 min
  15. Classifier prompts with definitions45 min
  16. Long documents: map then reduce45 min
  17. Asking clarifying questions40 min
  18. What a system prompt can hide40 min
  19. A/B testing two prompts45 min

  Module quiz

• Tool Use and AgentsPractice

  0/22 lessons

  1. Function calling50 min
  2. The agent loop55 min
  3. Agent design patterns55 min
  4. The MCP idea55 min
  5. Safe tool design55 min
  6. Module assignment: The safe order helpdesk agent55 min
  7. Parallel tool calls and result ordering40 min
  8. Writing tool descriptions the model can use40 min
  9. Forcing and limiting tool choice40 min
  10. Retrieval as a tool45 min
  11. Human checkpoints45 min
  12. Agent memory45 min
  13. Planning and re-planning40 min
  14. Budgets and timeouts for runs45 min
  15. Idempotent tools40 min
  16. Sandboxing code execution45 min
  17. Multi-agent handoffs40 min
  18. Subagents with their own tools45 min
  19. Reading agent traces35 min
  20. Browser and computer-use agents35 min
  21. Limiting tool result size40 min
  22. Error messages the model can act on40 min

  Module quiz

• LangChainPractice

  0/14 lessons

  1. What LangChain is50 min
  2. Prompt templates and output parsers55 min
  3. Multi-step chains and memory55 min
  4. When not to use LangChain50 min
  5. Module assignment: Ticket triage pipeline90 min
  6. Document loaders45 min
  7. Chat models and message types40 min
  8. Binding tools to a chat model45 min
  9. Stateful graphs50 min
  10. Callbacks and tracing hooks45 min
  11. Retrievers and vector store wrappers45 min
  12. Runnable methods: invoke, stream, batch35 min
  13. Fallbacks and retries40 min
  14. Dependency layout and version pinning40 min

  Module quiz

• Local Models with OllamaPractice

  0/18 lessons

  1. Why run a model locally50 min
  2. Install Ollama and run a model55 min
  3. Calling Ollama from code55 min
  4. Quantization and hardware55 min
  5. Choosing between local and cloud55 min
  6. Module assignment: Local-first gateway60 min
  7. Reading model tags and variants40 min
  8. Context length settings45 min
  9. Keep-alive and unloading40 min
  10. Local vision models40 min
  11. Tool calling with local models45 min
  12. Getting valid JSON from a local model40 min
  13. Benchmarking tokens per second40 min
  14. Running Ollama in Docker40 min
  15. Binding to a network address safely35 min
  16. Model storage and disk space35 min
  17. LoRA adapters explained45 min
  18. A small local model in CI tests40 min

  Module quiz

• Hugging FacePractice

  0/15 lessons

  1. The Hugging Face Hub50 min
  2. Hosted inference from TypeScript55 min
  3. Running models in JavaScript with transformers.js55 min
  4. Embedding models from the Hub55 min
  5. Module assignment: FAQ search60 min
  6. Dataset cards and licences45 min
  7. Tokenisers from the Hub45 min
  8. Text classification pipelines40 min
  9. Zero-shot classification45 min
  10. Named entity recognition40 min
  11. Summarisation and translation models40 min
  12. Image classification in the browser45 min
  13. Speech recognition with Whisper-style models45 min
  14. Pinning model revisions35 min
  15. Caching models and offline use40 min

  Module quiz

• RAG: Retrieval-Augmented GenerationPractice

  0/19 lessons

  1. What RAG is and why it exists50 min
  2. Vector search50 min
  3. Chunking documents50 min
  4. Better retrieval with hybrid search and reranking55 min
  5. Building RAG over documents55 min
  6. Module assignment: A tested RAG pipeline55 min
  7. Context packing within a budget40 min
  8. Citations that can be checked45 min
  9. HyDE: searching with a drafted answer40 min
  10. Parent-document retrieval40 min
  11. Metadata filters and access rules45 min
  12. Ingesting PDFs, tables and scans45 min
  13. Incremental re-indexing and deletes40 min
  14. Answer faithfulness checks45 min
  15. Multi-hop questions and iterative retrieval45 min
  16. Graph RAG basics45 min
  17. Code-aware chunking and retrieval45 min
  18. Caching embeddings and answers45 min
  19. Reading retrieval failures45 min

  Module quiz

• Guardrails and EvalsPractice

  0/17 lessons

  1. Input and output checks50 min
  2. Defending against prompt injection55 min
  3. Building an eval set55 min
  4. Using a model as judge55 min
  5. Regression testing and monitoring55 min
  6. Module assignment: A guarded support bot with an eval gate55 min
  7. Detecting and redacting personal data45 min
  8. Scope limits for a bot40 min
  9. Abuse controls40 min
  10. Output leakage checks40 min
  11. Red teaming a bot45 min
  12. Precision, recall and F145 min
  13. Labelling guidelines and review queues45 min
  14. Agreement between labellers45 min
  15. Flaky evals and repeated runs45 min
  16. Safety policy as tests45 min
  17. Tuning thresholds for guard checks45 min

  Module quiz

• Project: AI Chat with Your DocsPractice

  0/5 lessons

  1. Spec and project setup50 min
  2. Build the indexing pipeline55 min
  3. Build the chat endpoint with sources55 min
  4. Chat page, evals and polish55 min
  5. Module assignment: Find it better55 min

  Module quiz

• Project: AI Support AgentPractice

  0/5 lessons

  1. Spec and design50 min
  2. Build safe tools55 min
  3. The agent loop with guardrails55 min
  4. Evals and launch checks55 min
  5. Module assignment: Cancellation and approval desk55 min

  Module quiz

• Project: AI Code ReviewerPractice

  0/6 lessons

  1. Spec and setup50 min
  2. Parse the diff55 min
  3. The review call and validated findings55 min
  4. Evals and posting to pull requests55 min
  5. Inline comments and hardening55 min
  6. Module assignment: Rules, ignores and limits55 min

  Module quiz

• Embeddings and Vector StoresPractice

  0/12 lessons

  1. Choosing an embedding model45 min
  2. Distance metrics in practice40 min
  3. Normalising vectors35 min
  4. How HNSW search works45 min
  5. Index settings and recall45 min
  6. Vector quantization40 min
  7. pgvector in Postgres45 min
  8. Hosted vector databases40 min
  9. A local store on disk45 min
  10. Upserts, deletes and versions45 min
  11. Embedding drift when you change models40 min
  12. Batching and rate limits for embeddings45 min

• Observability, Cost and LatencyPractice

  0/12 lessons

  1. What to log for model calls35 min
  2. Tracing a multi-step request35 min
  3. Token and cost accounting40 min
  4. Budgets, alerts and hard caps40 min
  5. Latency: first token versus total35 min
  6. Cutting latency40 min
  7. Exact and semantic caches45 min
  8. Routing from cheap to strong models40 min
  9. Quality drift on dashboards40 min
  10. Load tests for cost and latency45 min
  11. Error budgets and fallbacks40 min
  12. Trace retention and access40 min

• AI Full-Stack AppsPractice

  0/17 lessons

  1. Spec for an AI feature in a web app35 min
  2. Server route that calls a model40 min
  3. Chat UI state in React45 min
  4. Saving conversations in Postgres45 min
  5. Accounts and sign-in40 min
  6. Scoping data to the current user40 min
  7. Per-user quotas40 min
  8. Ingestion jobs with status45 min
  9. Feature flags for model changes40 min
  10. Human confirm before AI actions40 min
  11. Image upload and vision in an app40 min
  12. Showing sources and citations in the UI40 min
  13. Feedback buttons feeding evals40 min
  14. Serverless deploys and timeouts40 min
  15. Config per environment40 min
  16. Responsive chat layout40 min
  17. Incident runbook for AI features45 min

• Machine Learning BasicsPractice

  0/16 lessons

  1. What Machine Learning Is35 min
  2. Features and Labels40 min
  3. Train, Validation and Test Sets35 min
  4. Linear Regression40 min
  5. Loss and Gradient Descent45 min
  6. Logistic Regression and Classification50 min
  7. Decision Trees and Random Forests50 min
  8. Overfitting and Underfitting45 min
  9. Regularization45 min
  10. Cross-Validation45 min
  11. Confusion Matrix and ROC Curves45 min
  12. Regression Error Metrics40 min
  13. Clustering Basics45 min
  14. Neural Network Intuition45 min
  15. Scikit-Learn Pipeline Project50 min
  16. Saving and Serving a Model45 min

• Python and Data BasicsPractice

  0/16 lessons

  1. Python syntax and core types40 min
  2. Functions and modules45 min
  3. Lists, dicts and sets45 min
  4. Comprehensions and iteration45 min
  5. Reading and writing files35 min
  6. Reading JSON and CSV40 min
  7. Virtual environments and pip45 min
  8. Errors and exceptions45 min
  9. Testing with pytest40 min
  10. NumPy arrays40 min
  11. Vectorised operations in NumPy45 min
  12. Pandas Series and DataFrames45 min
  13. Cleaning messy data45 min
  14. Grouping and joining tables45 min
  15. Plotting data with matplotlib and pandas35 min
  16. Small analysis project55 min

Pick your options above to see the matching modules.