Model I/O is the foundation of LangChain’s input/output workflow. It orchestrates how prompts are formatted before reaching the LLM and how raw text responses are parsed, validated, and transformed into structured data.Documentation Index
Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
Use this file to discover all available pages before exploring further.
Why Model I/O Matters
Prompt engineering and response parsing are two sides of the same coin. A generic, one-line prompt often produces ambiguous or incomplete outputs. Conversely, a carefully crafted prompt written in the LLM’s preferred syntax and semantics leads to more accurate, relevant responses.High-quality prompts can significantly reduce hallucinations and improve the reliability of your language-model applications.
- Structure Prompts
Prepare and format input text—using templates, variables, and conditional logic—so the LLM understands your intent. - Parse Responses
Extract, validate, and convert the LLM’s raw output into schemas or data structures your application can consume.
Core Model I/O Tasks
| Task | Purpose |
|---|---|
| Prompt Engineering | Design templates, inject context, and apply best practices to guide the LLM’s output. |
| Response Parsing | Use validators, regex, and type schemas to transform model text into reliable data formats. |