Build real-world LLM, RAG, and agentic AI applications — not just prompts.
Why This Workshop Matters
Generative AI has gone from novelty to core infrastructure in just two years. Enterprise surveys show that:
- 65% of enterprises are already using generative AI tools in their tech stack, up from just 11% at the start of 2023.
- 96% of companies now see GenAI as a key enabler across departments.
- Generative AI spending and venture funding have exploded, with startups raising tens of billions of dollars and enterprise AI spending growing more than 6× in a year.
At the same time, recent studies from MIT and others show a harsh reality: around 95% of generative AI projects fail to create measurable business impact — not because the models are weak, but because systems are poorly designed, badly integrated, or never move beyond “toy demo” status.
Modern AI roles (AI Engineer, LLM Engineer, GenAI Consultant) increasingly demand practical skills in:
- Building apps on top of LLM APIs
- Retrieval-Augmented Generation (RAG)
- Using frameworks like LangChain / orchestration tools
- Vector databases & embeddings
- Agents and tool-calling workflows
This workshop is designed exactly for that gap: to take someone who knows ML/DL and teach them how to engineer robust, testable generative AI systems that actually integrate into real-world workflows.
Call us to learn more about this generative AI & LLM engineering workshop
Overview
Title
Generative AI & LLM Engineering Workshop
Duration
3 days (full-day, in-person, instructor-led)
Level
Intermediate — for participants who:
- Already know Python
- Understand basic ML concepts and deep learning at a high level
- Are comfortable with APIs, JSON, and basic software engineering workflows
Purpose
To equip participants with hands-on skills to design, build, and evaluate generative AI applications using LLMs, RAG, and emerging agentic patterns — in a way that is maintainable, testable, and deployable.
Core Focus Areas
- LLM fundamentals & prompting that goes beyond “try things”
- LLM APIs & basic app scaffolding
- Retrieval-Augmented Generation (RAG) with vector databases
- Tools, function calling & agent-style workflows
- Guardrails, evaluation, and failure modes
- A small but real capstone GenAI application
Core Tools / Concepts
- LLM APIs (e.g., OpenAI / equivalent providers)
- Embeddings & vector databases (e.g., Chroma / Pinecone / similar)
- Orchestration frameworks (e.g., LangChain-style patterns)
- JSON tools, function calling, simple evaluation strategies
(Specific stack can be adapted to client preferences.)
Who This Workshop Is For
This workshop is ideal for:
- AI / ML engineers moving from classic ML/DL into generative AI systems
- Software engineers / backend developers who want to integrate LLM-powered features into products
- Data scientists & analytics engineers who want to build internal GenAI tools, assistants, and RAG systems
- Technical product / platform folks who need to understand what is realistically buildable with LLMs today
Recommended prerequisites
- Solid Python and basic CLI skills
- Familiarity with HTTP APIs, JSON, and environment variables
- Basic ML / deep learning literacy (you know what a model, training, and inference are)
What Participants Will Be Able To Do
By the end of the 3 days, participants will be able to:
- Explain how modern LLMs work at a useful, engineering-oriented level: tokens, context windows, temperature, system vs user messages.
- Design effective prompting strategies and patterns (few-shot, tools, structured output) instead of random prompt tweaking.
- Build LLM-backed applications that expose APIs or simple UIs (CLI / web) and handle errors, timeouts, and retries.
- Implement RAG pipelines: chunking, embeddings, vector search, and retrieval strategies for domain-specific knowledge.
- Use agents / tool-calling patterns to orchestrate multi-step workflows and integrations.
- Apply basic evaluation & guardrails: prompt-based tests, scenario checks, safety filters, and monitoring.
Design and deliver a small end-to-end generative AI project that goes beyond chat and actually solves a concrete task.
3-Day Detailed Agenda
Day 1 — LLM Foundations, Prompting & Core Patterns
Session 1: How LLMs Actually Work (for Engineers)
- From classic deep learning to foundation models: pretraining, fine-tuning, instruction-tuning (high-level)
- Tokens, context windows, and why “context management” is one of the main engineering challenges
- Model selection: frontier vs open-source, latency vs quality vs cost
- Safety and policy constraints as part of system design
Hands-on focus (conceptual + light code)
- Inspect tokenization and context length in practice
- Compare outputs for different temperatures, top-k, and system instructions
Session 2: Practical Prompt Engineering (Beyond Tricks)
- Structuring prompts: roles, instructions, constraints, and examples
- Prompt “design patterns”:
- Classification & routing
- Extraction & transformation
- Summarization & synthesis
- Chain-of-thought & step-by-step reasoning (when and how)
- Classification & routing
- Designing prompts for structured JSON output for downstream code
Hands-on focus
- Design prompts for 3 different task types
- Implement basic structured output parsing and error handling
Session 3: Building Your First LLM-Powered Micro-App
- Basic app blueprint: request → LLM → post-processing → response
- Using API keys, environment configs, rate limits
- Logging, observability, and quick debugging strategies
Hands-on focus
- Build a minimal LLM micro-service or CLI utility (e.g. summarizer, classifier, helper assistant)
- Add simple guardrails: allowed actions, max tokens, basic content checks
Day 1 Outcomes
Participants can:
- Call an LLM API confidently and control behavior via parameters and prompts
- Build small, single-call LLM tools with structured output that can plug into real systems
Day 2 — Retrieval-Augmented Generation & Knowledge Integration
Session 4: Why RAG Exists & When To Use It
- Limits of static LLMs: outdated knowledge, hallucinations, and domain specificity
- RAG vs fine-tuning: trade-offs in cost, speed, control, and privacy
- RAG pipeline concepts:
- Ingestion & chunking
- Embeddings & vector indices
- Retrieval & ranking
- Generation with retrieved context (Zero To Mastery)
- Ingestion & chunking
Hands-on focus
- Compute embeddings for a small document set
- Store and query them in a vector database (or simple in-memory index)
Session 5: Building a Simple RAG Question-Answering System
- Chunking strategies (by section, by tokens, by semantics)
- Choosing metadata & filters (source, date, type)
- Prompt templates for retrieved context (stuffing, map-reduce, etc.)
- Handling “I don’t know”, confidence, and citation of sources
Hands-on focus
- Build a basic RAG app that answers questions over a small knowledge base
- Add source citations into the generated answers
Session 6: Making RAG Less Fragile
- Common RAG failure modes: missing recall, irrelevant snippets, long-context confusion
- Improving retrieval: hybrid search, re-ranking, better metadata, filters
- Addition of lightweight business logic around LLM calls (pre/post-processing)
Hands-on focus
- Run simple experiments: vary chunk size, retrieval top-k, and prompt variants
- Compare quality and note patterns to guide future RAG design
Day 2 Outcomes
Participants can:
- Design and implement a working RAG pipeline for a contained domain
- Reason about retrieval quality and adjust design instead of only “fixing prompts”
Day 3 — Tools, Agents, Evaluation & Capstone
Session 7: Tools, Function Calling & Agentic Workflows
- From “single call” LLM apps to multi-step workflows
- Function calling / tool-calling: letting the LLM decide when to call APIs, DBs, or services
- Orchestration basics (LangChain-style or equivalent): chains, tools, memory, routing (NVIDIA Training Solutions)
- Agents as an emerging pattern: planning, acting, reflecting — and when they’re overkill
- Industry data: rising adoption of “agentic AI” in enterprises. (McKinsey & Company)
Hands-on focus
- Define one or two tools (e.g. search, calculator, internal API stub)
- Implement a simple agent-style flow that uses tools under LLM control to answer a non-trivial request
Session 8: Evaluation, Guardrails & Responsible Usage
- Why most GenAI projects fail in production: lack of evaluation & integration discipline (Tom’s Hardware)
- Types of eval:
- Unit-style tests with fixed prompts
- Scenario tests (structured inputs/expected behaviors)
- Human-in-the-loop review and red teaming
- Unit-style tests with fixed prompts
- Guardrails:
- Content filters & policies
- Allow/deny lists
- Simple safety layers around tools and data
- Content filters & policies
- Observability: logging prompts, responses, latencies, and failures
Hands-on focus
- Write a few lightweight prompt-level tests (e.g. “never leak this info”, “always answer in X style”)
Add basic logging/metrics and design a simple review loop
Session 9: Capstone — End-to-End Generative AI Application
Teams design and implement a small but complete GenAI system, such as:
- A domain-specific assistant (e.g. internal policy Q&A, technical helper)
- A content workflow tool (e.g. structured brief → drafts → refinement pipeline)
- A data-exploration helper with tool-calling to hit a fake or real API
Suggested steps
- Problem framing: Who is the user? What problem are we solving?
- System design: Which pieces do we need — base LLM, RAG, tools, UI/API?
- Implementation:
- Integrate LLM API
- Add retrieval or tools if needed
- Implement guardrails and minimal evaluation checks
- Integrate LLM API
- Demo & discussion:
- Short 5–7 minute demo per team: architecture, decisions, and live run
- Group feedback on reliability, safety, and extension ideas
- Short 5–7 minute demo per team: architecture, decisions, and live run
Day 3 Outcomes
Participants walk away with:
- A functioning GenAI application they’ve designed and implemented end-to-end
- Concrete experience making trade-offs between simplicity, power, and reliability
A strong mental model for how to go from “LLM API” to real system in their own organizations
Call us to learn more about this generative AI & LLM engineering workshop
Key Outcomes & Takeaways
After this 3-day workshop, participants will:
- Have practical, reusable patterns for building LLM and RAG applications
- Understand where GenAI fits — and where it doesn’t — in real systems
- Be able to prototype and iterate on generative AI ideas quickly, without creating unmaintainable spaghetti
- Know how to apply basic evaluation and guardrails so their systems are safer and more predictable
Leave with project code, notebooks, and architecture sketches they can extend, share, or add to a portfolio
FAQs
1. Is this a “prompt engineering only” course?
Answer: No. Prompting is covered, but the focus is on engineering full systems: RAG, tools, agents, evaluation, and integration.
2. Do I need deep learning math for this?
Answer: No. Some familiarity with ML/DL concepts helps, but the emphasis is on using existing models and APIs effectively, not training foundation models from scratch.
3. Will we train our own LLM?
Answer: No. We work with pre-trained models via APIs or open-source deployments, which is how most real-world systems are built today due to cost and complexity.
4. Can this workshop be customized to our industry or stack?
Answer: Yes. Datasets, tools, and case studies can be aligned with finance, healthcare, government, education, or internal platforms, and adapted to your preferred cloud or provider.
Ready to Take the Next Step?
- Schedule a call with our trainer
Haris Aamir
Trainer
Lincoln School