Google AI With Python: Set Up a Small, Testable Workflow

Google's Gemini API has an official Python SDK that lets you call a model, process the response, and integrate AI into a Python project with a small amount of code. The key to a useful workflow is starting small: one function, one verified output, before adding complexity. Tools like fenbs keep the integration decisions and project context outside the code, so connected AI assistants and teammates start each session knowing what was agreed.

This guide uses the google-generativeai package, which is the official SDK. Check the current documentation at https://ai.google.dev/gemini-api/docs for the latest package name and API changes.

Install the SDK and authenticate

Install the SDK with pip:

pip install google-generativeai

Authenticate using an API key from Google AI Studio. Store the key in an environment variable rather than putting it in the code:

import os
import google.generativeai as genai

genai.configure(api_key=os.environ["GEMINI_API_KEY"])

If the key is missing, the SDK will raise an error on the first API call rather than at import. Add an explicit check at startup so the failure is clear. For key creation, see the Gemini API key guide.

Write a small, testable function first

Wrap the API call in a function that takes a clear input and returns a clear output. A function you can call with a test value is easier to check than a script that does many things at once:

def summarize_text(text: str) -> str:
    model = genai.GenerativeModel("gemini-1.5-flash")
    response = model.generate_content(f"Summarize in two sentences: {text}")
    return response.text

Call it with a known input and check the output manually before connecting it to anything else. A function you have verified once is a foundation you can build on. A script you have never run end-to-end in isolation is a source of surprises.

Handle errors before adding complexity

The API returns errors for rate limits, invalid inputs, and service issues. Handle these explicitly rather than letting them propagate as unhandled exceptions. The SDK raises specific exception types you can catch:

import google.api_core.exceptions as gexc

try:
    result = summarize_text(input_text)
except gexc.ResourceExhausted:
    print("Rate limit hit; try again shortly.")
except gexc.InvalidArgument as e:
    print(f"Invalid input: {e}")

A workflow that fails silently, or produces empty output without raising an error, is harder to debug than one that fails loudly. Add logging at the error boundaries from the start.

Verify outputs before acting on them

A language model output is a suggestion, not a guarantee. Before an AI output triggers an action, a write to a database, an email send, or a file change, add a verification step. That step can be a human review, a format check, or a constraint test.

For a summary function, check that the output is a non-empty string and falls within a reasonable character range. For a function that extracts structured data, validate the result against a schema before using it. The how to use AI at work guide covers verification as part of practical AI workflows.

Testing the workflow end to end

Once the individual function works, test the full path from input to output. Send a realistic input, check that the model responds as expected, verify that your output handling processes the response correctly, and confirm that any downstream action, a file write, a database update, an API call, completes without errors.

Create a small set of test inputs that cover the normal case, an edge case like a very short input, and an invalid input. Running this set manually after each code change takes less than a minute and catches regressions before they reach anyone else. A workflow that works in isolation often breaks when connected to the rest of the system.

Keep the test inputs in a file alongside the code rather than generating them fresh each run. Stable test inputs make failures reproducible. A test that passes sometimes and fails other times is harder to diagnose than one that always uses the same input.

For a broader view of how Python AI tools fit into a team workflow, see the how to use AI at work guide.

Documenting the decisions that shaped the workflow

A Python AI workflow accumulates design decisions: why you chose a particular model, why the prompt is worded as it is, why the output is handled a certain way. These belong in a shared project record, not only in code comments.

Code comments explain what the code does. A shared project record explains why decisions were made and what alternatives were considered. When you return to the code in three months, or hand it to a colleague, the why is often what they need. For more on the Google AI ecosystem, see the Google AI tools guide.

How to do this with fenbs

1. Create a free account. Add a project for the Python integration.
2. Add tasks for each step: installing the SDK, writing the first function, running the test set, and adding error handling.
3. Open Decisions and rules and record the approved model version, input restrictions, and verification requirements.
4. Connect a supported AI assistant from Integrations. It reads the project decisions before helping with the code.
5. After each working session, record what was completed and what the next step is. The next session starts from that record rather than from memory.