Slides¶

This section contains lecture slides covering LLM fundamentals, deployment, and prompting techniques.

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Lesson 44: State of the art in generative AI¶

Overview of the current LLM landscape, model architectures, and foundational concepts.

Topics covered:

Decoder-only transformers
Tokenization and autoregressive generation
Training approaches
Current state-of-the-art models

Location: slides/lesson_44_state_of_the_art.md

Lesson 45: LLM deployment¶

Practical approaches to deploying and serving LLMs in production and development environments.

Topics covered:

Inference servers (Ollama, llama.cpp)
Model quantization and optimization
CPU/GPU memory management
API interfaces

Location: slides/lesson_45_llm_deployment.md

Lesson 46: Prompting fundamentals¶

Introduction to prompt engineering and basic techniques for effective LLM interaction.

Topics covered:

Prompt structure and formatting
Zero-shot and few-shot learning
Chain-of-thought reasoning
System prompts and role definition

Location: slides/lesson_46_prompting_fundamentals.md

Lesson 47: Advanced prompting¶

Advanced techniques for complex tasks and improving model outputs.

Topics covered:

Self-consistency and multiple reasoning paths
Reflection and critique
Decomposition and step-by-step reasoning
Prompt optimization strategies

Location: slides/lesson_47_advanced_prompting.md

Lesson 48: LangChain basics¶

Introduction to building structured LLM applications with LangChain.

Topics covered:

Chat models and LLM wrappers
Chat prompt templates and structured prompts
Output parsers (string, JSON, Pydantic)
Basic chains and composition with LCEL

Location: slides/lesson_48_langchain_basics.md

Lesson 49: LangChain advanced features¶

Advanced LangChain patterns: conversational memory, the RAG pipeline, and agents.

Topics covered:

Conversational memory strategies (ConversationBufferMemory, ConversationSummaryMemory, etc.)
Document loading, splitting, and embedding
Vector stores and retrievers (RAG pipeline)
Agents and the ReAct pattern
Tools and LangChain agent components

Location: slides/lesson_49_langchain_advanced.md

Lesson 50: Fine-tuning, RLHF, and model alignment¶

How pre-trained base models are adapted into instruction-following assistants through supervised fine-tuning and reinforcement learning from human feedback.

Topics covered:

Base model behaviour vs. instruction-tuned behaviour
Supervised fine-tuning (SFT) on instruction/response pairs
LoRA and QLoRA: parameter-efficient fine-tuning
RLHF pipeline: reward models, PPO, and preference data
Direct Preference Optimization (DPO) as an RLHF alternative
Practical VRAM requirements for consumer and server GPUs

Location: slides/lesson_50_finetuning_alignment.md

Lesson 51: Benchmarking and evaluating LLMs¶

How to measure LLM output quality using automated text metrics, standardised benchmarks, and LLM-as-judge scoring.

Topics covered:

Why evaluation is hard (no ground truth, multiple quality dimensions)
Automated text metrics: ROUGE, BLEU, and BERTScore
Standard benchmarks: MMLU, HellaSwag, GSM8K, HumanEval, TruthfulQA
Leaderboards, data contamination, and safety evaluation
LLM-as-judge: rubric scoring, pairwise comparison, and failure modes
Evaluation frameworks: HuggingFace evaluate, lm-evaluation-harness, RAGAS

Location: slides/lesson_51_evaluating_llms.md