LangChain, LlamaIndex and AI Frameworks: concepts and vocabulary | Complete Generative AI: From Foundations to Production
A self-paced 26-week learning path with 15 modules: AI foundations, Python, machine learning, transformers, LLMs, prompts, APIs, embeddings, RAG, frameworks, agents, customization, multimodal AI, Spring Boot with React, and production LLMOps. Includes 15 practical assignments and three portfolio projects.
Prerequisites: Begin with basic computer skills. Programming is introduced in Weeks 2–3. Use a local Python environment for coding labs; Java and React experience helps in Weeks 21–22. Model downloads, provider accounts and deployment resources are optional learner-managed dependencies. The 26-week schedule is suggested; reading estimates exclude independent project work.
Learning goals
Framework architecture
Prompt and model abstractions
Retrievers and vector stores
Conversation state and memory
Document pipelines
Tracing and observability
Understand the foundations
Frameworks package recurring integration patterns such as model adapters, retrievers and document pipelines. LangChain and LlamaIndex provide abstractions for building applications around models and data; they are dependencies to evaluate rather than prerequisites for understanding RAG. Start with an explicit data flow and contracts so replacing one adapter does not rewrite authorization or evaluation. Choose one framework for this lab and use its version-matched documentation instead of combining snippets from incompatible releases.
Connect the ideas
Conversation state belongs to an authenticated user and conversation ID, with size, retention and access limits. A global mutable history can leak messages across users. Separate short-term conversation context from durable user preferences and evidence indexes. Traces should show step names, latency, retries and source IDs while redacting sensitive content and credentials. A framework memory component does not by itself establish consent, ownership, deletion guarantees or grounding.
Checkpoint: explain these concepts in your own words, then describe one failure that the design must detect. Use synthetic or permissioned data throughout the course.
01. Introduction to Artificial Intelligence · Week 1
02. Python Programming for Gen AI · Weeks 2–3
03. Mathematics and Machine Learning Foundations · Week 4
04. Deep Learning and Transformers · Weeks 5–6
05. Large Language Models (LLMs) · Weeks 7–8
06. Prompt Engineering · Weeks 9–10
07. Generative AI APIs and Application Development · Week 11
08. Embeddings and Vector Databases · Weeks 12–13
09. Retrieval-Augmented Generation (RAG) · Weeks 14–15
10. LangChain, LlamaIndex and AI Frameworks · Week 16
11. AI Agents and Agentic AI · Weeks 17–18
12. Fine-Tuning and Model Customization · Week 19
13. Multimodal Generative AI · Week 20
14. Gen AI with Java Spring Boot and React · Weeks 21–22
15. Production Gen AI, LLMOps and Capstone · Weeks 23–26