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GenAI Roadmap for 2026
Note: Learn GenAI in 2026
Level 1 — Foundations of GenAI and Transformers
- What is Generative AI, and how it’s different from traditional ML
- Transformer architecture (attention, positional encoding, decoder stacks)
- Tokens, embeddings, and positional context
- Pretraining vs fine-tuning vs instruction tuning
- Inference with pre-trained models (e.g., LLaMA, Mistral, Mixtral, Phi-3)
- Understanding tokenization and model vocabulary (e.g., SentencePiece, BPE)
Level 2 — Language Model Behavior and Prompting
- Prompt engineering basics (zero-shot, few-shot, CoT, ReAct)
- Role prompting, context design, and persona injection
- Advanced prompting methods (Tree-of-Thought, Graph-of-Thought, WebGPT)
- Temperature, top-k, top-p, beam search — decoding strategies
- Prompt compression and optimization techniques
- Guardrails and adversarial prompting defense (OpenAI function calling guardrails, NeMo Guardrails)
Level 3 — Retrieval-Augmented Generation (RAG)
- What is RAG and when to use it
- Chunking strategies (semantic, fixed size, recursive)
- Embedding models (OpenAI, Cohere, BGE, E5, GTE, Jina Embeddings)
- Vector DBs (FAISS, Weaviate, Qdrant, LanceDB, PGVector)
- RAG pipelines (SimpleRAG, Multi-RAG, HybridRAG, GraphRAG)
- Evaluating RAG output (faithfulness, hallucination, groundedness)
- Fine-tuning embedding models for retrieval (contrastive learning)
Level 4 — LLMOps and Tool Integration
- Intro to LLMOps vs MLOps
- LangChain, LlamaIndex, Dust, Haystack, Marvin, CrewAI
- LangGraph: event-driven, graph-based agent workflows
- Tool calling with OpenAI (function calling, JSON mode, tool_choice)
- Auto tool selection and dynamic routing
- OpenAI tool integration vs Anthropic tool use
- Synthetic data generation using agents
Level 5 — Agents and Agentic Frameworks
- What are agents and why do we need them
- Types of agents (tool-using, multi-hop, planning, recursive)
- ReAct vs Plan-and-Solve vs AutoGPT-style agents
- Action-observation loops and memory grounding
- Simple agent construction using LangChain Agents
- Building autonomous loops with LangGraph, CrewAI, and MetaGPT
- Autonomous evaluation loops using LM-as-a-Judge
Level 6 — Agent Memory, State & Orchestration
- Types of memory: Buffer, Summary, Entity, Vector
- Episodic vs persistent memory
- Context window strategies and context compression
- Memory via Redis, Chroma, or LangChain Memory classes
- Event-driven memory updates in LangGraph
- Function calling-based memory updates
- Combining symbolic memory with vector memory for reasoning agents
Level 7 — Multi-Agent Systems and Collaboration
- What is multi-agent collaboration and when it matters
- Architectures: Hub-and-Spoke, Decentralized, Hierarchical
- Message passing and communication protocols
- Multi-agent planning (e.g., CrewAI, AutoGen, DSPy teams)
- Conflict resolution and alignment in agent teams
- Applications: agents as research assistants, financial bots, dev teams
- Agent grading and self-play loops for training
Level 8 — Evaluation, Feedback Loops, and RL
- LM-as-a-Judge (LUNA-2, OpenAI Evals, Anthropic Claude Evaluator)
- Pairwise and unary comparison techniques
- Building reward models from user preferences
- RLHF, RLAIF, and RLVR — when and how to apply
- Grading reasoning chains with teacher verifiers
- Supervised fine-tuning on evaluator-graded data
- Using feedback signals to retrain agents in production
Level 9 — Protocols, Safety, and Advanced Alignment
- Model Context Protocol (MCP) and how it structures agent memory
- Action-to-Action Protocol (A2A) for autonomous agents
- Safety-first designs: Constitutional AI, verifiable agents, red teaming
- Traceability and logging in LangGraph/LLMOps stacks
- Guardrails and safe output validation (Nvidia NeMo Guardrails, GuardrailsAI)
- Autonomous policy updates through inner-loop retraining
- Self-verifying agents for open-ended generation
Level 10 — Build, Optimize & Deploy in Production
- App frameworks: Gradio, Streamlit, Dash, Chainlit
- Serving agents: FastAPI, Modal, Replicate, RunPod
- Quantization and model compression (GGUF, QLoRA, AWQ)
- Cost optimization using small language models (Phi-3, TinyLlama)
- Infrastructure: Docker, serverless agents, GPUs vs CPUs
- Prompt caching and vector cache optimization
- Observability: LangSmith, Arize, Trulens, Weights & Biases