Large Language Models (LLMs) are systems trained to predict the next token in text—an ability that powers summarization, reasoning, and instruction-following at the heart of agentic AI. This post explains LLMs in plain terms, clarifies training vs. inference, shows why tokens and context windows matter, and highlights strengths (fluency, flexibility) and pitfalls (hallucinations, bias, stale knowledge). By the end, you’ll know how to call an LLM via an API, interpret outputs critically, and decide when to add grounding and guardrails.
What Is a Large Language Model?
Plain-language explanation of LLMs and why they matter for agentic AI.
- Definition and intuition (patterns over text).
- Training vs. inference.
- Tokens and context windows.
Popular LLM Families
- OpenAI GPT, Claude, Llama, Mistral (brief distinctions).
- Open vs. closed models: trade-offs.
How LLMs Are Used
- Chat, summarization, extraction, reasoning, tool use.
- APIs, SDKs, playgrounds.
Strengths and Limitations
- Strengths: language fluency, rapid prototyping.
- Limitations: hallucinations, bias, outdated knowledge.
- Mitigations: grounding with retrieval, guardrails, evaluation.
Quick Start Next Steps
- Make a first API call.
- Experiment with prompts (instructions, few-shot).
- Log outputs; note error cases.
In the next post, we’ll connect LLMs to tools and memory with LangChain.