Click any module to expand. This is NOT a prompt engineering course — it's a DEVELOPER course for building, deploying, and scaling GenAI applications. Every topic names specific tools, includes code patterns, and connects to production use cases.
Async, decorators, Pydantic, token counting — the Python patterns GenAI apps use
Decorators (@retry, @cache for API wrappers), generators (yield for streaming LLM responses token-by-token), context managers for API sessions. Type hints with Pydantic for validating LLM inputs/outputs. dataclasses and TypedDict for structured prompt templates. These aren't abstract — they're the patterns every GenAI application uses daily.
Dictionaries for conversation history ({"role": "user", "content": "..."}), custom classes for prompt templates with variable substitution. Token counting with tiktoken library — calculate cost BEFORE sending. Conversation windowing: keep last N messages within context limit. Data structures that connect your code to the LLM API contract.
asyncio + aiohttp for concurrent LLM API calls: process 100 documents simultaneously instead of sequentially (10x throughput). async/await pattern, semaphores for rate limiting. Streaming responses with async for. Without async, GenAI apps feel slow; with async, they feel instant.
Retry on 429 (rate limit) with exponential backoff using tenacity. Structured logging with structlog: log every API call with model, tokens, latency, cost. Log prompt + response pairs for debugging hallucinations. Production logging ≠ print() — it's searchable, structured, and cost-trackable.
Token counting BEFORE API calls with tiktoken (avoid paying for over-limit prompts). Response caching: hash(prompt) → cached_response. Prompt compression: reduce tokens without losing meaning. Cost tracking: calculate $/request, $/user, $/feature. The optimizations that turn a $10K/month bill into $2K/month.
OpenAI, Anthropic, Google APIs + FastAPI for serving AI applications
OpenAI: client.chat.completions.create(model, messages, temperature, tools). Anthropic: client.messages.create(). Google Gemini: genai.GenerativeModel(). Streaming with stream=True. Structured output: response_format={"type": "json_object"} or function calling for guaranteed JSON. LiteLLM for provider-agnostic switching. The APIs every GenAI developer calls daily.
FastAPI: @app.post("/chat") with Pydantic models. StreamingResponse for token-by-token output. WebSocket for real-time chat. Background tasks for long operations. Request validation before hitting LLM. The backend that serves your GenAI application to users.
Retry with exponential backoff (tenacity). Circuit breaker for API failures. Fallback: OpenAI down → switch to Anthropic. Request queuing with Redis. Timeout handling. Health checks. The reliability engineering that makes GenAI apps production-ready.
API key management with environment variables or secret managers. Per-user rate limiting. Input validation (reject oversized prompts before API call). Cost tracking: log model + tokens per request. Model selection by task: GPT-4o-mini ($0.15/1M) for classification, GPT-4o ($2.50/1M) for reasoning — 16x cost difference.
Transformers, prompt engineering, text generation, evaluation metrics
Self-attention: Q, K, V matrices → attention scores → weighted sum. Multi-head attention for parallel relationship capture. Positional encoding. Feed-forward + layer norm + residuals. Encoder (BERT — bidirectional) vs decoder (GPT — autoregressive). Implementing self-attention from scratch in PyTorch.
RNN/LSTM → Word2Vec → BERT (2018, bidirectional encoder) → GPT-2/3 (autoregressive decoder, scaling laws) → GPT-4/Claude/Gemini (multimodal, million-token context). Understanding the evolution explains WHY current architectures work and what their limitations are.
NER with spaCy and Hugging Face. Sentiment analysis: VADER → TF-IDF+LR → BERT → zero-shot LLM. Text classification pipeline. Summarization: extractive vs abstractive. Each technique has a sweet spot: spaCy for speed, BERT for accuracy, LLM for flexibility.
Zero-shot, few-shot, chain-of-thought, role-based, system prompts, output format control, prompt chaining, negative prompting. The 10 techniques covered in detail with before/after examples and trade-offs for each approach.
Greedy (deterministic), beam search (explore paths), top-K/top-P sampling (controlled diversity), temperature (0=factual, 1=creative). Repetition penalty. Understanding these parameters turns "AI gives garbage" into "AI gives what I need."
BLEU (translation), ROUGE (summarization), BERTScore (semantic similarity), perplexity (model quality). Human evaluation for factuality/helpfulness. LLM-as-judge: use GPT-4 for scalable evaluation. Choosing the right metric per task.
PyTorch, LoRA/QLoRA fine-tuning, vLLM serving, ONNX deployment
Tensors, autograd, nn.Module, training loop (zero_grad → forward → loss → backward → step), DataLoader, GPU training. The framework every GenAI system is built on.
Implementing self-attention, multi-head attention, and positional encoding from scratch. Building a mini-GPT. Understanding implementation enables debugging fine-tuning and reading research papers.
Full fine-tuning (expensive) vs LoRA (0.1-1% parameters, matches quality) vs QLoRA (4-bit base + LoRA — 7B models on 16GB GPU). PEFT library: LoraConfig, get_peft_model. Instruction fine-tuning with (instruction, input, output) triplets. The techniques that make LLM customization accessible.
Model serialization, ONNX export, quantization (FP32→INT8). vLLM: PagedAttention for 2-4x faster serving. Model registry with MLflow/HF Hub. A/B testing model versions. The pipeline: fine-tune → quantize → serve → monitor.
GANs, VAEs, Transformers (GPT/BERT/T5), diffusion models, optimization
Generator vs discriminator adversarial training. StyleGAN for faces, Pix2Pix for translation. Training challenges: mode collapse, instability. GANs are less prominent in 2025 (replaced by diffusion) but provide foundational generative AI intuition.
Encoder → latent distribution → decoder. Reparameterization trick. Loss: reconstruction + KL divergence. Use cases: anomaly detection, data augmentation, representation learning. A principled probabilistic generation framework.
Encoder-only (BERT — classification, NER). Decoder-only (GPT, LLaMA — generation). Encoder-decoder (T5, BART — seq2seq). Mixture of Experts (GPT-4, Mixtral — scale without proportional compute). Which architecture for which task.
Forward: add noise over T steps. Reverse: learn to denoise step by step. Stable Diffusion: latent diffusion + CLIP encoder + VAE decoder. ControlNet for guided generation. LoRA for style fine-tuning. The technology behind DALL-E 3, Midjourney, Stable Diffusion.
Knowledge distillation (student from teacher — DistilBERT: 60% size, 97% quality). Pruning (remove unimportant weights). Quantization (FP32→INT4). Flash Attention (O(N) memory). Speculative decoding (2-3x faster). The techniques that make large models deployable on real hardware.
Vector databases, LangChain pipelines, hybrid search, evaluation, production deployment
OpenAI text-embedding-3-small, sentence-transformers. ChromaDB (simple, local), Pinecone (managed), Weaviate (hybrid), FAISS (speed). Cosine similarity. Choosing by: cost, scale, hybrid search needs.
Load docs → chunk (recursive, semantic) → embed → store → query → retrieve top-K → inject into prompt → generate with context. LangChain: document loaders, text splitters, retrievers, chains. Chunking strategy determines retrieval quality.
Keyword (BM25) + semantic (embeddings) with Reciprocal Rank Fusion. "error 403 nginx" needs keyword; "fix website access issues" needs semantic. Hybrid beats either alone. Re-ranking with cross-encoders for precision.
Retrieval: recall@K, MRR. Generation: faithfulness, relevance, hallucination rate. RAGAS framework for automated evaluation. Context precision with re-ranking. The metrics that prove your RAG system works.
Caching (embeddings + frequent queries), incremental ingestion, quality monitoring over time, user feedback loops, horizontal scaling. The engineering that transforms "RAG demo" into "RAG product."
Multi-agent, tool integration, LangGraph, CrewAI, human-in-the-loop
Supervisor (manager routes to specialists), swarm (peer-to-peer), pipeline (sequential). CrewAI for role-based teams. LangGraph for graph-based orchestration with conditional routing and shared state.
Function calling (OpenAI) / tool use (Anthropic). Tool definition: name + description + JSON Schema params. Tools: web search (Tavily), code execution, database, APIs. MCP (Model Context Protocol) for enterprise connectivity. Description quality → agent decision quality.
ReAct loop: Thought → Action → Observation → repeat. Planning: decompose complex goals. Self-correction: evaluate own output, retry. LangGraph conditional edges for dynamic workflow control. Autonomy levels: full auto → human-at-checkpoints → human-approves-all.
LangGraph (most flexible), CrewAI (simplest for teams), AutoGen (Microsoft, code tasks), OpenAI Agents SDK. Framework comparison and when to use which. LangSmith for debugging agent execution traces.
Approval gates for high-impact actions. Feedback loops for quality improvement. Escalation when uncertain. Guardrails: NeMo Guardrails, Guardrails AI for input/output/action boundaries. "Safe autonomy" with human-defined policies.
CLIP, LLaVA, image generation, video, multimodal RAG
CLIP: zero-shot image classification from text. LLaVA / GPT-4V / Claude Vision: LLMs that see images. Visual QA, image description, OCR. Building multimodal chatbots.
Stable Diffusion, DALL-E 3. ControlNet for spatial guidance. LoRA for style customization. Practical: product photography, synthetic data, marketing assets.
Sora, Runway Gen-3, Kling. Current: 5-30 second clips. Applications: short content, demos, educational. Fastest-evolving modality.
Text + image + audio unified. Medical: X-ray → AI report. E-commerce: product image → auto-listing. Multimodal RAG: embed images + text together. The convergence of all modalities.
Whisper STT, ElevenLabs TTS, Suno music, voice agents
Whisper: 99 languages, open-source, runs locally. Model sizes tiny→large. Fine-tuning on domain audio. Deepgram, AssemblyAI as API alternatives. Speaker diarization.
ElevenLabs (highest quality, voice cloning from 30s). OpenAI TTS, Coqui (open-source). Voice parameters: speed, pitch, stability. Ethical: voice cloning consent.
Suno, Udio for text-to-music. Meta AudioCraft (open-source). Sound effects. Quality: good for prototyping and background, not yet studio-grade.
STT (Whisper) → LLM → TTS (ElevenLabs) = voice agent. Real-time latency optimization: streaming STT + streaming LLM + streaming TTS. Voice interfaces that feel natural.
MLflow, W&B, CI/CD, Docker, model monitoring, prompt versioning
MLflow/W&B: log model, temperature, prompt template, metrics, cost per run. Prompt versioning: track templates like code. "Which prompt version gave best results?" requires tracking.
CI/CD: push → evaluate → deploy if quality meets threshold. Blue/green, canary deployments. Auto-rollback on quality degradation. Continuous evaluation cycle.
Docker multi-stage builds. docker-compose: FastAPI + Redis + vector DB. Kubernetes for scaling. GPU pod scheduling. Serverless for API-based models.
Response quality sampling, latency tracking (p50/p95/p99), cost monitoring, prompt injection detection, user feedback loops, alerting on quality degradation.
vLLM, semantic caching, GPU optimization, cost management
Horizontal scaling behind load balancer. Auto-scaling on queue depth. Queue-based architecture with Redis/SQS. Graceful degradation: fall back to smaller model under load.
Semantic caching (GPTCache): similar questions → cached response (cosine similarity > 0.95). Exact-match caching with Redis. Embedding caching for static docs. Reduces API costs 30-60%.
vLLM PagedAttention (2-4x throughput). Continuous batching. KV-cache optimization. TensorRT-LLM for NVIDIA inference. Multi-GPU tensor parallelism. The engineering for cost-effective self-hosted LLMs.
Model selection by task (16x cost difference GPT-4o-mini vs GPT-4o). Prompt compression. Caching. Batch processing. Self-hosted vs API break-even at ~$5-10K/month. Cost dashboards: $/user, $/feature.
Chatbots, code assistants, content platforms, knowledge systems
Customer support: RAG + order lookup + ticket creation + escalation. Multi-turn with memory. Guardrails: stay on topic, don't over-promise. Channel integration: web, WhatsApp, Slack. The most deployed enterprise GenAI app.
Copilot architecture. Cursor: AI-native editor. Custom code assistants fine-tuned on company codebase. Code review bots, test generation, documentation generation.
Blog: topic → outline → draft → edit (multi-agent pipeline). Marketing copy in multiple tones. Email campaigns. Localization. Brand voice via system prompt. Content moderation before publishing.
RAG over company docs (Confluence, SharePoint). Employee self-service Q&A. Enterprise semantic search. Knowledge graph construction. Competitive intelligence automation. The enterprise app with clearest ROI.
Design, build, test, optimize, deploy, and present a complete GenAI application
Choose problem: RAG system, AI assistant, multi-agent workflow, or creative AI app. System architecture diagram, component selection, API design, evaluation plan. Architecture review: present choices, defend trade-offs, incorporate feedback.
End-to-end build: data → processing → model → API → frontend → deployment. Code quality: modular, typed, error-handled, logged, tested. Git workflow with meaningful commits and PR reviews. Portfolio-ready code.
Automated evaluation suite (faithfulness, relevance, hallucination). Load testing. Latency profiling. Cost analysis at scale. A/B testing prompt/model versions. Caching and optimization improvements measured against baseline.
Live demo showing real queries and edge cases. GitHub: clean code, comprehensive README, deployment docs. Deployed application with live URL. Blog post explaining decisions and learnings. "Here's my deployed GenAI app — try it" — the strongest AI Engineer portfolio statement.
Multi-document RAG with hybrid search, re-ranking, agentic routing, evaluation with RAGAS, Streamlit frontend, deployed on Railway.
CrewAI/LangGraph: Researcher → Analyst → Writer agents. Web search + code execution + report generation. Guardrails + evaluation.
Whisper STT → LLM processing → ElevenLabs TTS. Real-time streaming. Domain-specific knowledge via RAG. Deployed with WebSocket.
Text + image generation. Stable Diffusion with LoRA styling. Blog post + matching images from single brief. Content moderation.
AI Engineer, GenAI Developer, LLM Engineer — job postings grew 400%+ from 2024 to 2025. Every company building AI products needs engineers to build RAG systems, deploy agents, and optimize costs. Demand far exceeds supply — qualified candidates get multiple offers at ₹15-40L+ packages.
TCS AI practice, Infosys Topaz, Wipro AI, startups (Ema, Gleen, Yellow.ai), and product companies (Flipkart, Swiggy, Razorpay) — everyone is building GenAI applications. Customer support bots, internal knowledge bases, code assistants, content generators. The market is massive and growing.
This module covers the COMPLETE stack: Python → APIs → NLP → DL → RAG → Agents → Multimodal → Audio → MLOps → Scaling → Enterprise. A developer who can build from LLM API call to deployed, evaluated, cost-optimized production system is the highest-value AI hire.
A deployed GenAI application with live demo, evaluation metrics, cost analysis, and clean GitHub repo is the strongest AI portfolio piece. Interviewers can INTERACT with your project. "Try my RAG agent" beats any number of certifications.
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