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Understanding LLMs
What language models are and how they actually work.
AI Fundamentals
Build a practical understanding of modern AI concepts and learn how they apply to institutional finance.
The Complete Guide to AI for Institutional Finance
A comprehensive introduction to modern AI, covering the concepts, terminology and technologies investment professionals should understand.
Beginner · 28 min read · Updated Aug 2026
Core concepts
Build the vocabulary
Short explainers for the ideas that keep showing up in institutional AI work.
Large Language Models (LLMs)
A beginner-friendly guide to the machinery behind ChatGPT, Claude, Gemini, and Llama.
Read guide →Prompt Engineering
A practical guide to steering large language models.
Read guide →Retrieval-Augmented Generation (RAG)
How grounding language models in external knowledge reduces hallucination and keeps responses current.
Read guide →AI Agents
How language models move from answering questions to pursuing goals, using tools, and completing multi-step tasks.
Read guide →Context Windows
How much text a language model can hold in mind at once, why bigger is not always better, and what happens when you run out of room.
Read guide →Knowledge Graphs
How structured relationships between entities give AI systems grounded, queryable knowledge that goes beyond text retrieval.
Read guide →Hallucinations
Why language models make things up, what causes confident but wrong output, and the techniques that reduce it.
Read guide →Reasoning Models
How test-time compute lets language models think through hard problems, and what the trade-offs look like.
Read guide →Embeddings
How text becomes numbers, why similarity in vector space mirrors similarity in meaning, and the infrastructure that makes retrieval possible.
Read guide →Model Context Protocol (MCP)
The open standard for connecting AI models to external tools, data sources, and services through a universal interface.
Read guide →WatchComing soon
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