Generative AI Training in Pune with Placement

What an LLM actually is — at the level you need to build with one, not the level you need to publish a paper. Cover the transformer architecture (attention, positional encoding, layer norm, residual streams) at an intuition level, the difference between pre-training, instruction tuning, and RLHF / DPO, the model-family landscape (Claude, GPT, Gemini, Llama, Mistral, Phi, Qwen), and why specific models suit specific use cases (Claude for long-context analysis, GPT for general tasks, Gemini for multimodal, Llama / Mistral for on-prem and privacy). Plus the discipline that good AI Engineers practice — what a model is bad at, where hallucinations come from, and the cost / latency / quality triangle.

Prompt engineering as a real engineering discipline, not magic incantations. Cover system prompts vs user prompts, few-shot prompting, chain-of-thought and the limits thereof, role / persona prompts (and why most production systems should not use them), JSON mode / structured output / Pydantic-AI for type-safe LLM responses, response constraints and validators, prompt versioning and A/B testing, and the discipline of writing prompts as code (in Git, with tests, with metrics). Hands-on with Claude, GPT, and Gemini APIs side-by-side so you internalise their differences.

The retrieval half of retrieval-augmented generation. Cover sentence embeddings (sentence-transformers, BAAI BGE, OpenAI text-embedding-3, Voyage), the geometry of embedding space, vector databases (pgvector for SQL-native, Chroma for prototyping, Weaviate / Pinecone / Qdrant for scale), distance metrics (cosine, dot product, L2), HNSW indexing, hybrid retrieval (BM25 + dense + reranking with cross-encoders), and chunking strategies (fixed-size, semantic, parent-document). We finish with a small semantic-search service against a real corpus of your choice.

RAG is the dominant production GenAI pattern in Pune product engineering — and the pattern most poorly executed in the field. Cover the full pipeline: ingestion (PDFs, HTML, code, images via vision models), chunking, embedding, storage, query rewriting, retrieval, reranking, prompt assembly, generation, citation, and response validation. The discipline that separates working RAG from theatre — chunk-size experimentation, retrieval recall measurement, hybrid retrieval, query rewriting for vague questions, and citation-aware generation. Build a production-style RAG service with measured retrieval quality.

Agentic workflows — LLMs that call tools, query databases, hit APIs, and loop until a goal is satisfied. Cover OpenAI function calling, Claude tool use, Anthropic's Model Context Protocol (MCP) for tool federation, the agent design loop (think → act → observe → think), task decomposition patterns, ReAct, error handling, and the honest limits of agents in production today (cost, latency, debuggability). Build a multi-tool agent that combines retrieval, computation, and external APIs against a real-world workflow.

Frameworks are tools, not religion. Cover the working subset of LangChain (chains, runnables, LangGraph for state machines), LlamaIndex (best-in-class for RAG over heterogeneous data), and Pydantic-AI (type-safe agent design — the framework Pune Python teams have been gravitating to in 2026). Honest comparison with hand-rolled stacks — when frameworks save time, when they obscure debugging, and when senior AI Engineers reach for vanilla SDK calls instead. Build the same small project in two of the three frameworks to feel the difference.

The week that separates senior AI Engineers from prompt-tinkerers. Cover offline evaluation — RAGAS for RAG quality (faithfulness, answer relevance, context precision / recall), DeepEval for unit-test-style LLM evaluation, human-in-the-loop evaluation patterns. Online observability — Langfuse / LangSmith for tracing, latency / cost / token tracking, structured logging that an SRE can actually debug. Guardrails — content filtering, prompt injection defence, jailbreak resistance, output safety, and the discipline of red-teaming your own system before shipping.

When and why to leave the frontier APIs. Cover the open-source landscape (Llama 3.x, Mistral, Phi-3, Qwen, IndicBERT for Indian languages), local serving (Ollama for dev, vLLM / TGI for production, llama.cpp for CPU / Apple Silicon), parameter-efficient fine-tuning (LoRA, QLoRA, PEFT) on a single consumer GPU or Colab Pro, dataset preparation for fine-tuning, training discipline (overfitting checks, evaluation), and the honest comparison — when fine-tuning a small open model beats prompting a frontier model (privacy, cost, latency, domain register) and when it doesn't.

Beyond text. Vision-language models (Claude vision, GPT-4-vision, Gemini multimodal) for document understanding, OCR, and visual QA. Image generation (DALL-E 3, Imagen, Stable Diffusion 3, Flux) and the production patterns (asset pipelines, brand-safe filtering, costs). Speech (Whisper for transcription, ElevenLabs for synthesis), code generation (Claude / GPT for code, the honest 'pair programmer' pattern), and the multimodal RAG pattern (text + image retrieval). Plus a cost-conscious approach since multimodal calls run 5–10× the cost of text-only.

The week that turns a notebook into a service. FastAPI as the de facto serving layer for GenAI in Python (async endpoints, Pydantic v2, JWT auth, streaming responses with Server-Sent Events, WebSocket for chat). Docker for containerisation, GitHub Actions CI/CD with model-call mocking in tests, observability (Langfuse + Prometheus + Grafana for production-grade GenAI telemetry), and FinOps for LLM calls — token budgets per request, caching (semantic cache + exact cache), prompt compression, and the patterns that cut a runaway OpenAI / Anthropic bill by 50–70% without changing model quality.

Two weeks of full-time capstone work plus structured interview preparation. Pick one of three capstone projects (see Capstone Projects). Mock interviews calibrated for Pune AI Engineer hiring panels — Persistent, BMC, Bajaj Finserv, Tiger Analytics, Fractal, BMW TechWorks, Mercedes-Benz R&D, TCS Research, Infosys Topaz, Mastercard Pune Tech Hub. Includes a system-design round (design a customer-support assistant for a BFSI company), an evaluation round (how would you measure if this RAG is working?), and a behavioural / product-thinking round. Resume / LinkedIn / GitHub polish included.