Forward Deployed Engineer (FDE) Master Program: GenAI, RAG, Agents & Enterprise AI – Live Training Demo

Forward Deployed Engineer (FDE) Master Program: GenAI, RAG, Agents & Enterprise AI – Live Training

1. What is a Forward Deployed Engineer?

• What is an FDE? | The intersection of engineering, consulting, and AI
•  FDE vs SWE | FDEs build *for* clients, SWEs build products
• The FDE Mindset | Speed > Perfection · Empathy > Assumptions
• FDE Skillset | AI + Data + Cloud + SE = the four pillars
• Delivery Method | => Concepts, MCQs and Labs

2. AI & GenAI Foundations

• What is Generative AI? | Models that *generate* content, not just classify
• AI → ML → DL → GenAI | The evolution and where we are now
• Transformer Architecture | Attention is all you need — simplifi ed mental model
• Tokens & Tokenization | How LLMs see text; why it matters for cost & limits
• Pre-training vs Fine-tuning vs RAG | Three ways to customize AI; when to use each
• Open vs Closed Models | Tradeoff s: control vs convenience
• AI Application Lifecycle | Idea → Prototype → Eval → Production → Monitor

3. LLM Fundamentals for FDEs

• How LLMs Work | Predict the next token — that’s it (but the implications are enormous)
• Context Window & Memory | The LLM’s “RAM” — what fi ts, what gets cut
• Hallucination | Why LLMs make things up; mitigation strategies
• Popular LLMs | Claude vs GPT-4o vs Gemini vs LLaMA — strengths & trade-off s
• Model Selection for FDEs | Match model to task: cost, speed, quality triangle
• API vs Self-Hosted | SaaS API for most; self-hosted for compliance/cost at scale
• Cost & Latency | Token pricing, batching, caching strategies

4. Prompt Engineering for Enterprise FDEs

• Prompt Engineering Overview | The #1 FDE superpower — 80% of AI quality comes from prompts
• Zero-Shot / Few-Shot / CoT & Prompt Chaining | Know when to give examples, when to reason step-by-step
• System Prompts & Role Design | Persona + Instructions + Constraints = reliable output
• Prompt Chaining | Break complex tasks into sequential prompt steps
• Structured Output (JSON Mode) | Force the LLM to return parseable data
• Temperature & Controls | Creativity vs precision; when to dial each
• Prompt Evaluation | A/B test prompts; measure output quality rigorously
•  Enterprise Patterns | Classifi cation, extraction, summarization, transformation

5. AI Engineering Core

• Embeddings and Vector Databases
• Chunking Strategies
• Indexing Methods
• Vector Databases
• Vector DB Performance Optimization
• What is RAG? Ground LLM responses in *your* data — eliminate hallucination
• RAG vs Fine-Tuning, RAG for knowledge; fi ne-tuning for style/format
• Data Ingestion, PDFs, databases, APIs, web pages → chunks → vectors
• Retrieval Strategies ,Top-K, MMR (diversity), re-ranking
• Metadata Filtering, Filter by date, category, author before vector search
• Advanced RAG, Self-query, HyDE, Corrective RAG, RAPTOR
• RAG Evaluation, Faithfulness, Answer Relevance, Context Recall (RAGAS)

6. AI Agents & Agent Orchestration

• What is an Agent? | LLM + Tools + Memory + Decision Loop |
• Tool Calling | Defi ne tools as JSON schema; LLM decides when/how to call them |
• ReAct Pattern | Reason → Act → Observe → Repeat |
• Agentic Design Patterns | Planning, Refl ection, Memory, Multi-step reasoning |
• Agent Memory | Short-term (in-context), Long-term (DB), Episodic (past runs) |
• Agent Observability | Trace every step; log tool calls; measure token usage |
• Lab: Research Agent
•  “`python
• # Build an agent that:
• # 1. Accepts a research question
• # 2. Searches the web (tool: web_search)
• # 3. Reads relevant pages (tool: read_url)
• # 4. Synthesizes fi ndings into a structured report
• # 5. Saves output to a fi le (tool: write_fi le)

7. Multi-Agent Systems & AI Frameworks

• Multi-Agent Architecture | Why one agent isn’t enough for complex enterprise tasks
• Supervisor-Worker | Coordinator delegates; specialists execute
• Peer Agents | Agents communicate directly; no central coordinator
• LangChain | Building blocks: chains, memory, tools, retrievers
• LangGraph | State machines for complex, stateful agent workfl ows
• Agent State Sharing | Shared memory and message passing between agents

8. MCP, AI Workfl ows & Automation

• What is MCP? | Standard protocol for LLMs to call external tools safely
• MCP vs API | MCP is context-aware and AI-native; REST APIs are generic
• MCP Components | Host (Claude Desktop), Client (app), Server (tool wrapper)
• Building an MCP Server | Expose any tool (DB, API, fi le system) via MCP
• AI Workfl ow Design | Directed graphs of AI steps + human checkpoints
• Automation Patterns | Trigger-based, event-driven, scheduled AI workfl ows

9. AI-Assisted Software Engineering

• AI-First SDLC | Prompt → Generate → Review → Refi ne → Ship (not: write → debug → repeat)
• Claude Code | Agentic CLI that reads/writes fi les, runs commands, fi xes bugs
• Cursor | AI-native IDE — inline generation, codebase-aware completions
• GitHub Copilot | Autocomplete + chat — best for line-by-line acceleration
• Rapid Prototyping | 0 → working demo in 2 hours using AI tools
• AI Code Review | Use AI to review your own PRs before submitting
• AI Debugging | “Here’s the error + stack trace — what’s wrong?”
• Lab: Build a Full Feature in 2 Hours Using AI Tools**
• • Challenge: Build a REST API with the following in under 2 hours using Claude Code:
  • POST /analyze — accepts text, returns sentiment + entities + summary
  • GET /history — returns last 20 analyses from SQLite
  • GET /health — health check endpoint
  • Full test suite (pytest)
  • Docker containerization
  • Rules: 3
  • You may not manually write more than 20 lines of code
  • All code must be generated or heavily assisted by AI
  • You must review and understand every line before shipping

10. Data Engineering for FDEs

• DE Fundamentals | Data is the fuel for AI — FDEs must be able to wrangle it
• ETL vs ELT | Modern: load fi rst, transform in the warehouse (ELT wins)
• Data Lake vs Lakehouse | Lakehouse = Delta Lake on object storage — best of both
• Medallion Architecture | Bronze (raw) → Silver (cleaned) → Gold (business-ready)
• Batch vs Streaming | Batch for history; streaming for real-time AI
• Data Pipelines | Ingestion → transformation → loading → validation
• Data Quality | Null checks, schema validation, freshness monitoring
• Platform Landscape | Databricks, Snowfl ake, dbt, Airfl ow, Kafka — when to use each
• AI + Data Integration | How AI pipelines consume data from your data platform
• Lab: End-to-End Data Pipeline with AI Transformation**
  • Scenario: Client has raw customer support tickets in S3 (CSVs).
  • Goal: Build a pipeline that:
  • Ingests raw CSVs to Bronze Delta table
  • Cleans and deduplicates → Silver table
  • Uses Claude to classify each ticket (category + priority) → Gold table
  • Exposes Gold table via SQL for BI dashboard
  • Stack: Python + pandas + delta-spark (local) + Claude API

11. Cloud Architecture for FDEs

• Cloud-Native Principles | Design for failure; scale horizontally; automate everything
• AWS Well-Architected | 5 pillars every FDE should know before touching client cloud
• AI Platform Deployment | Where AI apps live: EC2, Lambda, ECS, Bedrock, SageMaker
• Microservices & APIs | Decompose AI apps into independently deployable services
• Docker for AI | Container your AI app → consistent, portable, deployable
• Serverless for AI | Lambda for lightweight inference; cost-eff ective for event-driven AI
• Security Fundamentals | IAM least-privilege; VPC isolation; secrets in AWS Secrets Manager
• CI/CD for FDEs | GitHub Actions → test → build → deploy on every merge

12. Enterprise AI + FDE Delivery

• Understanding the Forward Deployed Engineer (FDE) delivery model
• Converting business problems into AI-driven technical solutions
• Requirements gathering and stakeholder discovery techniques
• Conducting effective client workshops and requirement analysis sessions
• Problem Translation Framework for Enterprise AI projects
• Designing AI solutions based on business objectives
• Building rapid AI prototypes and proof-of-concepts (POCs)
• Technical storytelling and solution presentation techniques
• Enterprise AI project planning and delivery best practices
• Communicating AI solutions to technical and non-technical stakeholders
• Creating technical briefs, solution proposals, and implementation roadmaps
• Portfolio building, case studies, GitHub projects, and LinkedIn branding
• Real-world client engagement and consulting scenarios
• End-to-end Enterprise AI solution delivery framework

13. Mini Project