This is a code-first walkthrough of what goes on under the hood of LLMs and generative AI, aimed at developers, security people, and operators who want to understand this stuff through code rather than diagrams. The examples use LangChain with Python and OpenAI, though the concepts apply to any framework.
Starting from a basic “hello world” LLM call, the first key concept is temperature: set it to zero for deterministic results, leave it open for creative variation. Context size limits how much text you can send, and models tend to forget information in the middle of long prompts. From there, prompt templates let you parameterize your queries – think of it as variable interpolation for LLMs, the equivalent of SQL parameter escaping.
The real power starts with RAG (Retrieval Augmented Generation). You load documents, split them intelligently (by markdown headers rather than arbitrary byte chunks), calculate embeddings for similarity matching, and store them in a vector database like Chroma. When a user asks a question, the system finds the most relevant document chunks via embedding similarity, stuffs them into the prompt with instructions like “use only this context, say you don’t know if the answer isn’t there,” and sends it to the LLM. The result: the model can answer questions about your private data without training on it.
API chains take this further – feed the LLM an OpenAPI spec and it generates the correct URL to call. Agents combine tools and reasoning: give the agent a search tool and a goal, and it iterates through action-observation cycles until it converges on a solution. This is the foundation for autonomous assistants.
On the operations side, callbacks track token usage and costs. Embedding-based caching avoids redundant API calls for similar queries. Ollama provides local LLM development with a Docker-like model file concept. For security, prompt injection is the biggest threat – a user can override system instructions by injecting “ignore previous instructions” into their input. Mitigation includes sandwich prompting, dedicated injection detection models from Hugging Face, and constitutional chains that enforce ethical principles. Toxicity checking uses specialized models. The key insight: don’t use just the LLM hammer for everything – combine it with purpose-trained smaller models for specific validation tasks.
Watch on YouTube — available on the jedi4ever channel
This summary was generated using AI based on the auto-generated transcript.