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Welcome to this lesson on interacting with large language models using LangChain. In this module we’ll focus on the Model I/O component — the part of LangChain responsible for invoking models and handling their inputs and outputs. You’ll learn how to call models, design effective prompts, and format or parse model responses for your applications.
The image is a welcome slide outlining three topics: understanding prompt engineering, formatting and transforming responses, and interacting with a large language model. The slide has colorful graphics and a gradient background.
What this lesson covers:
  • How to invoke a model (model calls).
  • How to craft prompts that get reliable outputs (prompt engineering).
  • How to format, parse, and transform model responses to suit your app.
By the end of this lesson you will be able to make model calls with LangChain, apply foundational prompt engineering techniques, and parse responses to integrate them cleanly into your application workflows.
The image features a section labeled "Prerequisites" with a blue gradient background, listing "Install the Libraries" and "Follow the Versions" with colored dots.
Prerequisites Before you begin, install the required Python packages and pin the package versions used in these examples. LangChain and related libraries evolve quickly; using the exact versions below helps avoid API and behavioral differences that can break examples.
Use the exact package versions listed to match the code and APIs in this lesson. Version mismatches are a common source of errors.
Install and configure Run these commands to install the packages and set your OpenAI API key as an environment variable:
Security best practices
Never commit API keys to source control. Store secrets in environment variables or a secrets manager. If you use a .env file during development, load it securely (for example with python-dotenv) and ensure the file is excluded from version control.
Verify your environment Check that the OPENAI_API_KEY environment variable is set before running the examples:
  • If the command prints a value, your key is already set and you can continue.
  • If it prints nothing, run the export command above (or set the key in your environment management tool).
In some managed KodeKloud workspaces the API key may already be configured. Confirm the variable before re-exporting. Quick troubleshooting
  • If you see import errors, confirm you installed the pinned versions.
  • If you receive authentication or rate-limit errors from OpenAI, confirm your API key is valid and has sufficient quota.
Next steps With the environment ready, proceed to the next lesson sections to:
  • Build prompt-driven interactions using LangChain.
  • Make model calls and inspect raw outputs.
  • Parse, validate, and transform model responses for use in downstream application code.
Links and references

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