LangChain
Interacting with LLMs
Types of Language Models
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.") |
Note
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, orassistant - Context History: Maintains the conversation transcript across turns
- Persona Configuration: A
systemmessage 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 |
Warning
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
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