Large Language Models: What Developers Actually Need to Know
A structured deep-dive into large language models — how they work, where they struggle, and what that means for people building with them today.
What this course covers
Most developers encounter LLMs through an API call and a few JSON responses. That works until it does not.
This webinar gets into the mechanics that actually matter when you are shipping something real. We will cover tokenization and why token limits bite you in unexpected places, how temperature and top-p sampling affect output consistency, and what attention mechanisms mean for long-context tasks. Not theory for its own sake, but the kind of understanding that tells you why your prompt worked yesterday and failed today.
Where production LLM systems break
Hallucination is the obvious problem, but the subtler issues are harder to catch: context window mismanagement, instruction-following drift across long conversations, and the gap between benchmark performance and real-world reliability. We will look at concrete failure cases from deployed applications, including retrieval-augmented generation setups that returned confidently wrong answers because the retrieval step was poorly scoped.
Prompt engineering gets a lot of attention, but fine-tuning decisions and model selection matter just as much. We will compare open-weight models like Mistral and LLaMA variants against hosted APIs across latency, cost, and control, so you can make a sensible choice for your use case rather than defaulting to the most expensive option.
Practical tooling covered
- LangChain and LlamaIndex for orchestration
- OpenAI, Anthropic, and Mistral API differences
- Evaluation frameworks: RAGAS, PromptFoo
- Observability with LangSmith
By the end you will have a clearer mental model of where LLMs are genuinely useful, where they are a liability, and how to structure your system so failures are catchable rather than silent.
Prerequisite: comfort with Python and basic REST API usage. No ML background required.Course programme
Session Outline
- Part 1 (30 min): How LLMs generate text, tokenization, sampling parameters, and context windows
- Part 2 (25 min): Failure modes in production, hallucination patterns, and RAG pitfalls with real examples
- Part 3 (25 min): Model selection, open-weight vs hosted, and cost-latency trade-offs
- Part 4 (20 min): Live demo: building a simple RAG pipeline with evaluation using RAGAS
- Part 5 (20 min): Q&A, architecture review of attendee projects
What you will receive after the session
Recording access for 60 days, annotated slide deck, and a reference guide covering prompt patterns and model comparison tables used during the demo.
Questions before enrolling?
Send us your details and a brief note about your background. Our team typically responds within one business day and can help you decide if this course fits your current level.