Generative AI Professional Certification Training Program

Learning Outcomes:

Explain the evolution from traditional ML to generative AI and foundation models

Differentiate between discriminative and generative modeling paradigms

Describe the Transformer architecture, self-attention mechanism, and positional encoding

Identify key LLM families (GPT, LLaMA, Gemini, Claude, Mistral) and their trade-offs

Topics Covered:

AI/ML landscape: supervised, unsupervised, and generative paradigms

History & milestones: GANs → VAEs → Transformers → Foundation Models

Transformer deep-dive: encoder-decoder, multi-head self-attention, positional encoding

Pre-training objectives: causal LM, masked LM, seq2seq denoising

Scaling laws, emergent abilities, and model benchmarks (MMLU, HumanEval, GPQA)

Open-source vs proprietary LLMs: licensing landscape (Apache 2.0 vs community licenses vs proprietary APIs)

Hands-on Lab Activities:

Lab 1: Explore tokenization with tiktoken — analyze token counts, vocabulary sizes, and encoding strategies across GPT-4o and open-source tokenizers; visualize token boundaries on code vs natural language

Lab 2: Run inference on a pre-trained LLM (LLaMA 3 / Mistral 7B) via Hugging Face Transformers — compare outputs with varying temperature, top-p, top-k, and repetition penalty settings

Learning Outcomes:

Design effective prompts using zero-shot, few-shot, and chain-of-thought techniques

Apply structured prompt patterns (ReAct, persona, meta-prompting) for complex reasoning

Implement context engineering strategies to manage what enters the LLM context window

Evaluate prompt quality using systematic scoring frameworks

Topics Covered:

Anatomy of a prompt: system instructions, user messages, assistant pre-fill

Zero-shot, one-shot, and few-shot prompting strategies with example selection

Chain-of-Thought (CoT), Tree-of-Thought, Self-Consistency, and Step-Back prompting

Prompt patterns: persona, template, meta-prompting, ReAct reasoning

Context engineering: managing retrieval context, tool outputs, memory, and system instructions within token budgets

Prompt injection, jailbreaking risks, and defensive prompting techniques

Prompt evaluation: relevance scoring, faithfulness checks, coherence metrics

Hands-on Lab Activities:

Lab 1: Build a prompt engineering workbench using OpenAI API — systematically test and compare zero-shot vs CoT vs few-shot across code generation, mathematical reasoning, and summarization tasks; log results with scoring rubrics

Lab 2: Implement a context engineering pipeline — given a 100K-token knowledge base, build a system that dynamically selects, prioritizes, and compresses context to fit within a 16K-token window while maximizing answer quality

Learning Outcomes:

Integrate OpenAI, Anthropic, and open-source LLM APIs into Python applications

Implement streaming responses, retry logic, rate-limit handling, and error recovery

Build LLM-powered applications using LangChain and LlamaIndex frameworks

Manage conversation context, memory types, and structured output parsing

Topics Covered:

OpenAI Chat Completions API: models, parameters, JSON mode, structured outputs

Anthropic Messages API: system prompts, tool use, extended thinking, prompt caching

Hugging Face Inference API and local model serving with Ollama

LangChain fundamentals: chains, prompt templates, output parsers, LCEL

LlamaIndex basics: data connectors, node parsers, indexing, query engines

Conversation memory: buffer, summary, token-window, and vector-store-backed memory

Structured output extraction: JSON mode, Pydantic models, Instructor library

Hands-on Lab Activities:

Lab 1: Build a multi-provider LLM gateway in Python that routes requests to OpenAI, Anthropic, or local Ollama based on task type — implement unified streaming, automatic retries with exponential backoff, and cost tracking per request

Lab 2: Create a structured data extraction pipeline using Instructor + Pydantic — extract product specifications from unstructured e-commerce descriptions into validated, typed JSON schemas with error handling and retry logic

Learning Outcomes:

Explain how text embeddings represent semantic meaning in high-dimensional vector space

Select embedding models based on task requirements and benchmark performance

Set up and query vector databases with metadata filtering

Implement semantic search and hybrid retrieval pipelines (dense + sparse)

Topics Covered:

Embedding evolution: Word2Vec → GloVe → BERT → Sentence-BERT → modern embeddings

Models: OpenAI text-embedding-3, Cohere Embed v3, BGE, E5, Jina

Evaluation: cosine similarity, MTEB, dimensionality and matryoshka embeddings

Vector DB architecture: HNSW, IVF-PQ, metadata filtering, namespaces

Chunking strategies: fixed, recursive, semantic, document-aware, parent-child

Hybrid search: dense + BM25 with reciprocal rank fusion

Hands-on Lab Activities:

Lab 1: Build a semantic code search engine — embed Python functions using OpenAI embeddings, store in ChromaDB with metadata (file, class, docstring), and retrieve relevant code by natural language queries with re-ranking

Lab 2: Benchmark chunking strategies (fixed 512-token vs recursive vs semantic) on a technical PDF corpus — measure retrieval quality using hit-rate@k, MRR, and nDCG metrics across 50 test queries

Learning Outcomes:

Architect end-to-end RAG pipelines for knowledge-grounded generation

Implement advanced retrieval: re-ranking, query transformation, multi-step retrieval

Evaluate RAG systems using RAGAS (faithfulness, relevance, context metrics)

Optimize RAG for latency, accuracy, cost, and hallucination reduction

Topics Covered:

RAG architecture: indexing → retrieval → augmentation → generation

Naive vs Advanced RAG: failure modes and improvements

Query transformation: HyDE, multi-query expansion, step-back prompting

Re-ranking: Cohere Rerank, BGE Reranker, ColBERT

Context strategies: stuffing, map-reduce, refine, tree-summarize

Evaluation with RAGAS

Hands-on Lab Activities:

Lab 1: Build an Advanced RAG pipeline using LlamaIndex — implement query decomposition, hybrid retrieval (vector + BM25 with reciprocal rank fusion), Cohere Rerank, and source citation over a multi-document technical knowledge base

Lab 2: Evaluate and optimize the pipeline using RAGAS — auto-generate a synthetic test set, measure all four RAGAS metrics, iterate on chunking size, retrieval top-k, and reranker threshold to improve scores by at least 15%

Learning Outcomes:

Explain limitations of vector-only RAG and when Graph RAG is a better fit

Build knowledge graphs from unstructured text using LLM extraction

Implement Graph RAG combining graph traversal with vector retrieval

Query KGs using natural language via LLM-generated Cypher

Topics Covered:

Vector RAG limitations: multi-hop, global summarization, entity resolution

Knowledge graph fundamentals: entities, relations, triples, property graphs

LLM KG construction: entities, relations, coreference

Microsoft GraphRAG: community detection and hierarchical summarization

Neo4j + LangChain integration and hybrid retrieval

Use case selection: vector vs graph vs hybrid

Hands-on Lab Activities:

Lab 1: Build a knowledge graph from a technical documentation corpus using LLM-based entity/relation extraction — store in Neo4j, visualize the graph, and implement natural language querying via LLM-generated Cypher

Lab 2: Implement a hybrid Graph RAG system — combine Neo4j graph traversal for multi-hop entity questions with vector retrieval for general queries; compare answer accuracy against pure vector RAG on a 30-question benchmark

Learning Outcomes:

Apply the decision framework: prompt vs RAG vs fine-tune

Prepare datasets for instruction tuning

Fine-tune an open-source LLM using QLoRA

Evaluate and distill models for cost optimization

Topics Covered:

Fine-tuning methods: LoRA, QLoRA, prefix tuning, adapters

Dataset formatting, filtering, deduplication, synthetic data

QLoRA training with PEFT/TRL and quantization

Training config and evaluation strategies

Model distillation and cost-performance trade-offs

Hands-on Lab Activities:

Lab 1: Fine-tune LLaMA 3 (8B) using QLoRA on a custom instruction dataset for domain-specific code generation — configure 4-bit quantization, train with SFT Trainer, and merge LoRA adapters into the base model

Lab 2: Implement model distillation — use GPT-4o to generate high-quality training data, fine-tune a smaller model (Mistral 7B) on the distilled dataset, and compare performance vs cost against the teacher model on a held-out benchmark

Learning Outcomes:

Explain the RLHF pipeline from SFT through reward modeling to PPO optimization

Implement Direct Preference Optimization (DPO) to align a fine-tuned model

Understand Constitutional AI, RLAIF, and emerging self-alignment techniques

Conduct systematic red-teaming to identify model safety failure modes

Topics Covered:

The alignment problem: why pre-training and SFT alone are insufficient for safe deployment

RLHF pipeline: SFT → reward model training → PPO optimization (conceptual deep-dive)

Direct Preference Optimization (DPO): eliminating the reward model, loss function intuition

Constitutional AI, RLAIF, and iterative self-alignment approaches

Preference data collection: human annotation guidelines, inter-annotator agreement, synthetic preference generation

Red-teaming methodology: systematic probing for harmful outputs, hallucinations, bias, and prompt injection vulnerabilities

Hands-on Lab Activities:

Lab 1: Align a model with DPO using TRL — take the SFT model from Module 7, construct a preference dataset (chosen/rejected pairs for helpfulness and safety), train with DPO Trainer, and evaluate alignment improvements

Lab 2: Red-team the aligned model — design a systematic evaluation covering toxicity, hallucination rates, prompt injection resistance, and refusal appropriateness; produce a structured safety scorecard

Learning Outcomes:

Leverage code-specialized LLMs for generation, completion, review, and refactoring

Integrate AI coding assistants (GitHub Copilot, Cursor, Claude Code) into development workflows

Build custom code generation and automated testing tools using LLM APIs

Assess and mitigate risks in AI-generated code: hallucinated APIs, license issues, security vulnerabilities

Topics Covered:

Code LLMs: CodeLlama, StarCoder 2, DeepSeek-Coder V2 — architecture, training data, and benchmark performance

AI coding assistants: GitHub Copilot, Cursor, Claude Code, Codeium — workflow integration patterns

Code generation patterns: function synthesis from docstrings, test generation, refactoring, code translation

Automated code review: bug detection, security vulnerability scanning, anti-pattern identification with LLMs

Documentation generation: docstrings, README files, API documentation from source code

Risks and limitations: hallucinated APIs, license compliance (copyleft contamination), security risks, over-reliance patterns

Hands-on Lab Activities:

Lab 1: Build an AI-powered code review bot — ingest a Python repository, use an LLM to analyze each function for bugs, code smells, type issues, and security vulnerabilities; output structured review comments in GitHub PR format

Lab 2: Create an automated test generation tool — given a Python module, generate pytest unit tests using an LLM, execute them, capture failures, feed errors back to the LLM for self-correction, and measure final code coverage

Learning Outcomes:

Work with multimodal models that process and generate text, images, and audio

Build applications using vision-language models (GPT-4o, Gemini, Claude Vision, LLaVA)

Implement text-to-image generation and image understanding pipelines

Integrate speech-to-text (Whisper) and text-to-speech capabilities into applications

Topics Covered:

Multimodal architectures: vision encoders (ViT, SigLIP), cross-attention fusion, early vs late fusion strategies

Vision-Language Models: GPT-4o, Gemini Pro Vision, Claude Vision, LLaVA — capabilities and API usage

Text-to-image generation: Stable Diffusion, DALL-E 3, Flux — diffusion model fundamentals and controlability

Image understanding: captioning, visual question answering, document/chart/diagram analysis with VLMs

Audio models: Whisper (speech-to-text), ElevenLabs / XTTS (text-to-speech), audio understanding

Emerging modalities: video understanding (Gemini, GPT-4o), video generation, and omni-modal models

Hands-on Lab Activities:

Lab 1: Build a multimodal document analyzer — use GPT-4o Vision API to extract structured data (tables, charts, key figures) from scanned financial reports and engineering diagrams; output validated JSON with confidence scores

Lab 2: Create an end-to-end voice-interactive assistant — integrate Whisper for speech input, an LLM for reasoning, and a TTS engine for spoken output; build a FastAPI endpoint supporting real-time audio streaming

Learning Outcomes:

Design autonomous AI agents with planning, reasoning, memory, and tool-use capabilities

Implement function calling and structured tool integration across OpenAI, Anthropic, and open-source APIs

Build stateful multi-step agent workflows using LangGraph with conditional branching

Integrate agents with external systems using Model Context Protocol (MCP) for standardized tool access

Topics Covered:

AI agent architecture: perception → reasoning → planning → action → observation loop

Function calling: OpenAI tools API, Anthropic tool use, parallel and sequential tool execution

ReAct (Reasoning + Acting) agents: thought-action-observation cycles, scratchpad management

LangChain agents: tool selection, agent executor, custom tool creation, structured tool outputs

LangGraph: stateful workflows with conditional edges, cycles, persistence, and checkpointing

Model Context Protocol (MCP): standardized tool integration, MCP servers, client architecture

Human-in-the-loop patterns: approval gates, clarification requests, escalation, and fallback strategies

Hands-on Lab Activities:

Lab 1: Build a ReAct research agent with custom tools — create an agent that can search the web, execute Python code, query a SQL database, and read files; orchestrate a multi-step research workflow that synthesizes findings into a structured report

Lab 2: Implement a LangGraph customer support agent — design a stateful workflow with branching (ticket classification → intent routing → knowledge retrieval → resolution → escalation), persistence across sessions, and human approval gates at critical decision points

Learning Outcomes:

Implement input validation, output filtering, and content moderation guardrails for LLM applications

Deploy PII detection, hallucination checks, and toxicity filtering in production pipelines

Use guardrail frameworks (NeMo Guardrails, Guardrails AI, LlamaGuard) to enforce safety policies

Design responsible AI governance: bias auditing, fairness testing, and compliance with AI regulations

Topics Covered:

Guardrail taxonomy: input guards (prompt injection detection, topic restriction, PII masking) vs output guards (hallucination detection, toxicity filtering, format validation)

NeMo Guardrails: Colang flows, topical rails, fact-checking rails, moderation rails

Guardrails AI (RAIL spec): validators, on-fail actions, structured output enforcement

LlamaGuard & ShieldGemma: safety classification models for content moderation

PII detection and redaction: regex + NER hybrid approaches, Microsoft Presidio integration

Hallucination detection: self-consistency checks, NLI-based verification, source grounding validation

Responsible AI: bias auditing, fairness metrics, EU AI Act basics, model cards, and transparency reporting

Hands-on Lab Activities:

Lab 1: Build a multi-layered guardrail system using NeMo Guardrails — implement input rails (prompt injection detection, topic restriction, PII masking), output rails (hallucination check via NLI, toxicity scoring, format validation), and test against an adversarial prompt suite of 50+ attack vectors

Lab 2: Integrate Guardrails AI into a RAG application — add Pydantic-based output validators, implement factual consistency checks against retrieved sources, build a PII redaction pipeline with Presidio, and generate a safety compliance report with pass/fail metrics

Learning Outcomes:

Design comprehensive evaluation strategies: LLM-as-judge, automated benchmarks, and human evaluation

Implement CI/CD-integrated prompt testing and regression detection pipelines

Deploy LLM applications to production with optimized inference and observability

Manage cost, latency, and reliability using caching, model routing, and fallback chains

Topics Covered:

LLM evaluation paradigms: reference-based metrics (BLEU, ROUGE), LLM-as-judge, human evaluation protocols

Automated testing: Promptfoo for prompt regression testing, DeepEval for unit-testing LLM outputs, CI/CD integration

A/B testing prompts and models: experiment design, statistical significance, guardrail-aware rollout

Serving frameworks: vLLM, TGI, Triton Inference Server — continuous batching, KV-cache, speculative decoding

Deployment: cloud APIs (AWS Bedrock, Azure OpenAI, GCP Vertex), self-hosted, serverless architectures

Inference optimization: quantization (GPTQ, AWQ), prompt caching (Anthropic, OpenAI), semantic response caching with Redis

Observability: LangSmith, Langfuse, Phoenix — distributed tracing, cost tracking, latency monitoring, error dashboards

Cost management: token budgeting, tiered model routing (complex → GPT-4o, simple → Haiku), fallback chains

Hands-on Lab Activities:

Lab 1: Build an LLM testing and evaluation pipeline — use Promptfoo to create a test suite with 30+ test cases across accuracy, safety, and format dimensions; integrate with GitHub Actions for CI/CD prompt regression detection; implement LLM-as-judge scoring with calibrated rubrics

Lab 2: Deploy a production-ready LLM application — serve a quantized model with vLLM behind FastAPI, instrument with Langfuse tracing, implement semantic caching with Redis, set up model routing (GPT-4o for complex queries, Haiku for simple ones), and build a Grafana-compatible monitoring dashboard tracking latency (TTFT, TPS), cost per query, and error rates

Learning Outcomes:

Understand the fundamentals of LLMOps for managing LLMs in production

Explore best practices and tools for deploying LLMs

Design deployment pipelines for Generative AI applications

Implement basic LLM deployment using containerization and orchestration

Integrate n8n for automating deployment workflows

Topics Covered:

Introduction to LLMOps: definition, importance, and lifecycle management of LLMs

LLM Deployment Best Practices: model serving, API endpoints, latency optimization

Containerization: Docker for packaging LLM applications

Orchestration: introduction to Kubernetes for scalable deployment

Model Serving Frameworks: TensorFlow Serving, TorchServe, Hugging Face Inference Endpoints

CI/CD Pipelines for LLMs: automating build, test, and deployment

n8n for workflow automation in AI deployments

Hands-on Lab Activities:

Hands-on: Containerizing a simple LLM application using Docker

Deploying a containerized LLM application to a local Kubernetes cluster (Minikube)

Setting up a basic CI/CD pipeline for an LLM application (e.g., using GitHub Actions)

Use n8n to create automated workflows for AI model deployments (e.g., automating model updates or monitoring tasks)

Learning Outcomes:

Architect and build a production-grade Generative AI application integrating multiple modules

Implement RAG (vector + graph), agentic tool use, guardrails, and observability in a single system

Demonstrate systematic evaluation with automated test suites and safety benchmarks

Topics Covered:

Project briefing: requirements specification, evaluation rubric, and deliverable checklist

Architecture design session: component selection, data flow diagramming, integration planning

Implementation sprint: guided build with mentor checkpoints at each integration stage

Testing & evaluation: functional testing, RAG evaluation (RAGAS), safety checks (guardrail test suite), load testing

Career pathways in Generative AI: roles (AI Engineer, LLM Ops, Prompt Engineer), certifications, and industry trends

Hands-on Lab Activities:

Capstone: Build an AI-Powered Enterprise Knowledge Assistant — a full-stack application integrating: (1) Advanced RAG with hybrid vector + graph retrieval and re-ranking over a multi-format document corpus (2) Agentic tool use with LangGraph — code execution, web search, database queries, and MCP-based integrations (3) Multi-layered guardrails — input validation, PII detection, hallucination checking, and output filtering (4) Production infrastructure — FastAPI serving, Langfuse observability, semantic caching, and model routing

Deliverables: working application, architecture document, Promptfoo test suite (30+ cases), RAGAS evaluation report, safety scorecard