Types of Language Models

1. Standard Text-Generation LLMs
2. Chat Models
Typical Chat Flow
Next Steps
References

Large Language Models (LLMs) power a wide range of AI applications—from single-prompt text generation to full conversational agents. In this lesson, we’ll compare the two main categories of LLMs and demonstrate how LangChain seamlessly integrates both.

1. Standard Text-Generation LLMs

Standard text-generation LLMs process a lone prompt and produce a completion. They’re ideal for tasks like:

Creative writing (poems, short stories)
Sentence or idiom completion
Code snippets and documentation generation

You send a prompt, the model applies its generation algorithm, and it returns a block of text.

Capability	Description	Example Command
Creative Writing	Generate narratives, poetry, scripts	`model.generate("Write a Haiku about spring.")`
Text Completion	Finish a partial sentence or paragraph	`model.generate("In a world where AI...")`
Code or Documentation	Produce code blocks or technical text	`model.generate("Implement quicksort in Python.")`

Standard LLMs excel in single-shot tasks but do not maintain conversational state across multiple inputs.

2. Chat Models

Chat models are LLM variants fine-tuned for multi-turn dialogues. Key differences include:

Roles: Messages are tagged as system, user, or assistant
Context History: Maintains the conversation transcript across turns
Persona Configuration: A system message sets tone, rules, or behavior

Typical Chat Flow

system: Sets behavior (e.g., “You are a helpful assistant.”)
user: User’s question or instruction
assistant: Model’s response

Feature	Standard LLM	Chat Model
Input Format	Single prompt	Role-based message list
Context Window	Single-turn only	Multi-turn with memory
Persona Control	Prompt engineering only	Dedicated `system` role

Keep an eye on the token limit for chat models—the full history contributes to usage and may incur additional cost.

Both standard and chat-based LLMs are fully supported in LangChain. You can switch between them using the same notebook and API patterns.

Next Steps

Now that you understand the distinction between standard text-generation LLMs and chat models, let’s dive into hands-on examples with LangChain:

Initialize a text-generation chain
Build a conversational agent with memory
Compare responses and performance

References

Watch Video

Setting up Environment

Getting Started with LLMs

⌘I

Introduction

Building Blocks of LLM Apps

Tips Tricks and Resources

Introduction to LCEL

Adding Memory to LLM Apps

Performing Retrieval

Implementing Chains

Using Tools

Building Agents

Conclusion

Interacting with LL Ms

Key Components of Lang Chain

Overview of Lang Chain

Types of Language Models

1. Standard Text-Generation LLMs

2. Chat Models

Typical Chat Flow

Next Steps

References

Watch Video

Introduction

Building Blocks of LLM Apps

Tips Tricks and Resources

Introduction to LCEL

Adding Memory to LLM Apps

Performing Retrieval

Implementing Chains

Using Tools

Building Agents

Conclusion

Interacting with LL Ms

Key Components of Lang Chain

Overview of Lang Chain

​1. Standard Text-Generation LLMs

​2. Chat Models

​Typical Chat Flow

​Next Steps

​References

Watch Video

1. Standard Text-Generation LLMs

2. Chat Models

Typical Chat Flow

Next Steps

References