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You can build a useful first LLM app with a small Python program: accept an input, send it to a model through an API, and show the response. Start with one provider and one request; add document retrieval, an interface, or an agent framework only when the app needs it.
What an LLM app does
At its simplest, an LLM app connects your program to a model. Your Python code collects an input, sends it to a model API, receives a response, and presents that response to a person or another part of your application. The provider’s SDK handles much of the API communication; your code still decides what to ask, how to handle the result, and what to do when a request fails.
Keep these decisions separate as the app grows: which model to use, whether to retrieve outside information, whether to use an agent workflow, and how to build the user interface. They solve different problems, and you do not need to adopt a framework to make a first request.
Build a first Python app with one API request
1. Prepare Python and install the SDK
Use Python 3.10 or later for the OpenAI Python SDK described in its official documentation, which says the library provides access to the OpenAI REST API from Python 3.10+ applications. Install the package in your project environment with:
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pip install openaiThe SDK’s documentation identifies the Responses API as its primary interface for interacting with OpenAI models. Because SDKs and API recommendations change, use the current official documentation for the model and API details when you turn an example into runnable code.
2. Keep the API key out of your code
Read credentials from environment configuration rather than putting a key in a Python file. The SDK reference recommends python-dotenv as one way to keep a key out of source control. Do not commit secrets in source files or notebooks, and do not expose them in screenshots. A local environment file can be convenient during development, but ensure it is excluded from version control.
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3. Send one request and inspect the result
Follow the current SDK’s minimal Responses API example to create a client, submit a prompt, and inspect the returned text. This first version should do little more: take one input, make one request, and display the output. Then improve the prompt and add error handling before introducing storage, retrieval, or a framework.
Chat Completions is also documented as a supported, earlier standard. Do not mix examples from different API styles without understanding which one you are using; follow current API guidance for a new project.
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Choose the right next step for the app
| Approach | Useful when | Trade-off |
|---|---|---|
| Direct provider SDK | You need a small app or want to understand the request-and-response flow. | Fewer abstractions and direct API access; you compose additional application logic yourself. |
| Framework | You need to organize multiple integrations, retrieval steps, or workflows. | Convenient building blocks introduce abstractions and framework-specific concepts. The available documentation does not establish a universal performance or answer-quality winner. |
| Hosted model API | You want to call a provider’s model without setting up a local model environment. | It depends on an external service. The sources cited here do not establish comparable current costs, privacy terms, or latency against local options; assess those for the provider and deployment you choose. |
| Local or broader model ecosystem | You want to explore models and tools beyond one provider’s API. | Model availability and infrastructure needs vary. The Hugging Face documentation lists tools such as Gradio for demos and web apps and smolagents for Python agents; these are options, not prerequisites. |
Make a chatbot answer questions from your documents
Plain generation answers from the information available to the model in the request and its learned parameters. If the app needs to answer from a maintained set of documents, retrieval augmented generation (RAG) adds a search step: find relevant passages and send them along as context for the model.
Prepare the knowledge base
Divide the documents into sections that can be retrieved individually, then create an embedding for each section. An embedding represents text in a form that can be compared with other text for relevance. Keep the source material organized so that you can inspect which passages the app selects.
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Retrieve at question time
- Embed the user’s question.
- Use that embedding to retrieve the most relevant knowledge-base sections.
- Include the retrieved sections in the model request as context, along with the question.
- Show the response, and inspect both the retrieved passages and the answer while developing the app.
This sequence follows the workflow in OpenAI’s guidance on using the OpenAI API for Q&A or chatbots. A document Q&A app is a useful next project because retrieval has a clear purpose: the model receives selected material rather than an entire large file appended indiscriminately to every prompt. Retrieval can supply relevant context, but it does not guarantee a correct answer. Try representative questions and check whether the passages support the response.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Add frameworks when complexity justifies them
Learn the direct request-and-response flow first. Then consider a framework if composing retrieval, tools, or multi-step behavior is becoming difficult to manage in your own code.
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LangChain’s Python tutorials include examples for PDF semantic search, RAG, SQL agents, and voice agents. Its learning materials also describe LangGraph primitives as a way to customize agent workflows more directly. That makes a reasonable progression: a direct API call, higher-level integrations when useful, and more customizable workflow building blocks when the application needs control. None is a required starting point.
For a demo or web interface, Hugging Face’s documentation lists Gradio; for Python agent experiments, it lists smolagents. These tools address different parts of an application, so choose them for a specific need rather than adding them all at once.
Test behavior and handle failures
A successful API response does not establish that an answer is accurate. Test questions a user is likely to ask, including cases where the supplied context does not contain the answer. For a document app, review the retrieved passages as well as the generated response: a poor retrieval result can leave the model without the information it needs.
Before expanding the app, handle request failures and make the user experience clear when a response cannot be produced. Keep the first version observable: you should be able to tell what input was sent, whether the request succeeded, and—if using retrieval—which sections were provided as context.
Where to learn next
Official documentation is enough to follow the core path. OpenAI offers learning tracks for building agents and developing AI apps at OpenAI Learn. LangChain’s tutorials and academy resources are available through its Python tutorials. Start with material that matches the next problem you are solving, rather than trying to learn every framework before building anything.
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