Topic
Foundation Models: Base vs Instruct vs Chat
9 posts on foundation models: base vs instruct vs chat — part of glossary on the n4n AI blog.
What is a foundation model?
A precise technical definition of foundation models, how they differ from fine-tuned variants, and what engineers need to know when building on top of them.
How RLHF turns a base model into ChatGPT
A practitioner's guide to RLHF — from preference data collection through reward modeling to PPO fine-tuning, with code, pitfalls, and tradeoffs.
How instruction tuning turns base models into assistants
A practical guide to instruction tuning — how it transforms base LLMs into usable assistants, with code examples, dataset strategies, and common pitfalls.
Foundation models explained: the GPT-5 and Claude era
A technical definition of foundation models in the GPT-5 and Claude era, covering architecture, training paradigms, and practical implications for engineers building LLM systems.
Foundation model vs fine-tuned model: what's different
A practitioner's comparison of foundation models versus fine-tuned models across capabilities, cost, latency, and operational trade-offs — with a clear verdict by use case.
Chat models vs instruct models: which one do you need
Understand the practical differences between chat models and instruct models — training objectives, prompting patterns, latency, cost, and when to use each.
Base models explained: raw next-token prediction
A base model is a raw language model trained only for next-token prediction. This explainer covers how it works, why it matters for engineers, and common misconceptions.
Base, instruct, and chat: picking the right model variant
A practical guide to choosing between base, instruct, and chat model variants for production LLM systems, with decision criteria and code patterns.
Base model vs instruct model: what's the difference
Understand the practical differences between base and instruct models, when to use each, and how they behave in production systems.
More topics in glossary
- Structured Outputs & JSON Mode19
- AI Agents Fundamentals12
- Hallucination in LLMs11
- Sampling Parameters: Top-p, Top-k & Penalties11
- Context Window & Context Length10
- Fine-Tuning Fundamentals9
- Model Families & Naming Conventions: GPT-5, Claude, Gemini 3, Llama 4, Mistral, DeepSeek, Qwen, Grok9
- Grounding & Fact-Checking in AI8
- LLM Benchmarks: MMLU, HumanEval, SWE-bench & GPQA8
- Max Tokens, Stop Sequences & Output Truncation8
- Quantization Formats: GGUF, GPTQ, AWQ & INT4/INT88
- Chain-of-Thought Prompting7