LLM Intermediate Topics
Simple apps, embeddings, vector databases, RAG, evaluation, prompt engineering, and agents.
Simple apps, embeddings, vector databases, RAG, evaluation, prompt engineering, and agents.
Intermediate LLM concepts explore how large language models integrate with real-world applications, structured data, semantic search, and automated reasoning. This includes building simple apps, using embeddings, storing vector data efficiently, implementing retrieval-augmented generation, evaluating models, engineering prompts, and creating agent-based systems.
Basic tools such as chat interfaces, summarizers, or text transformation apps.
Numerical representations of text used for semantic search and clustering.
Special databases optimized for storing and searching embeddings efficiently.
Retrieval-Augmented Generation improves model accuracy using external knowledge.
Techniques to measure quality, correctness, and safety of LLM outputs.
Crafting structured instructions to guide model responses effectively.
LLM-driven systems capable of autonomous planning, reasoning, and tool use.
Text is captured and embedded for semantic understanding.
Relevant documents retrieved from a vector database.
LLM combines query with retrieved context.
Quality checks ensure accurate and helpful output.
Use embeddings and vector search to find meaningfully similar content.
RAG-powered systems answer domain-specific questions accurately.
Autonomous AI workers handling research, analysis, and task execution.
Not always. Small datasets can work with in-memory search.
For fast-changing information, yes. For style-specific tasks, fine-tuning may help.
Tools provide capabilities. Agents use tools autonomously to achieve goals.
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