Conditional Generation Setup with OpenAI: Controlling AI Text

Why Set Up Conditional Generation?

Conditional generation lets you steer an AI model’s output by adding specific instructions—or conditions—to your prompt, like “write formally” or “keep it brief.” With OpenAI’s models, such as text-davinci-003, this turns a basic request into a customized response shaped by your needs. The model’s transformer architecture, trained on vast datasets up to April 2023, adjusts its token-by-token predictions based on these conditions, producing text that fits your goals—see What Is Generative AI and Why Use It?.

Why use it? You gain control over tone, style, and length, essential for tasks like technical docs or creative writing. It’s versatile, switching from casual to formal with a tweak, and efficient, tapping into OpenAI’s free tier ($5 credit, ~2.5 million tokens) or low cost (~$0.002/1000 tokens). Setting this up with logging and verification lets you monitor and refine results. Let’s set it up smoothly.

Step 1: Install Necessary Libraries

Begin by installing the libraries you’ll need in Python to work with OpenAI and manage your outputs.

Preparing Your Python Environment

You’ll need Python 3.8 or higher and pip, the package manager for fetching libraries. Open a terminal—VS Code is a great choice with its built-in terminal, editor, and debugging tools—and check your setup:

python --version

Expect “Python 3.11.7,” the stable version as of April 2025, known for solid performance and library support. If it’s missing, download it from python.org. During setup, tick “Add Python 3.11 to PATH” so python runs from any terminal spot, avoiding path hassles. This gives you a reliable base for scripting.

Next, check pip:

pip --version

Look for “pip 23.3.1” or a similar version. If it’s not there, install it with:

python -m ensurepip --upgrade
python -m pip install --upgrade pip

Pip links to PyPI, the Python Package Index, a huge hub with over 400,000 packages, pulling in what you need for your Python environment.

Now, install the libraries:

pip install openai python-dotenv logging

Here’s what each does:

• openai: The OpenAI library, version 0.28.1 or newer, under 1 MB, lets you call OpenAI’s API to generate text, your core tool for this setup.
• python-dotenv: A small utility, about 100 KB, loads API keys from a .env file, keeping them safe and out of your code.
• logging: A built-in Python module (no extra install needed), tracks outputs and events, useful for monitoring what happens.

Check the install with:

pip show openai

You’ll see “Name: openai, Version: 0.28.1” or similar, confirming it’s ready. These libraries prepare you to generate and manage text.

How It Works

• python --version: Shows your Python version, like 3.11.7, confirming it’s recent enough to handle openai and other tools smoothly.
• pip --version: Verifies pip is set up, letting you grab libraries from PyPI without any trouble.
• pip install ...: Pulls openai, python-dotenv, and logging into your environment. openai connects to the API, python-dotenv keeps your key secure, and logging records your runs.
• pip show openai: Confirms the openai library is installed, giving you a quick check that it’s good to go.

This sets up your Python toolkit naturally—next, configure OpenAI access.

Step 2: Configure OpenAI API Access with Prompt Conditioning

Configure OpenAI API access and add conditioning to your prompt, guiding the text output naturally.

Setting Up API Access

Get an API key from platform.openai.com—sign up for the free tier ($5 credit)—and head to “API Keys.” Create a key, name it (e.g., “CondGen2025”), and copy it, like sk-abc123xyz. Set up a project folder:

mkdir CondGenBot
cd CondGenBot

Add a .env file:

OPENAI_API_KEY=sk-abc123xyz

Create cond_setup.py:

import openai
from dotenv import load_dotenv
import os

# Load API key
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")

# Conditioned prompt
prompt = "Write a formal explanation of neural networks."
response = openai.Completion.create(
    model="text-davinci-003",
    prompt=prompt,
    max_tokens=50
)

# Display output
text = response.choices[0].text.strip()
print("Conditioned Output:")
print(text)

Run python cond_setup.py, and expect:

Conditioned Output:
Neural networks are computational models inspired by biological neural systems, comprising interconnected nodes organized in layers. They process input data through weighted connections, enabling pattern recognition and predictive tasks via iterative training.

How It Works

• .env file: Stores your OPENAI_API_KEY safely, keeping it out of your script so it doesn’t leak if shared.
• load_dotenv(): Pulls the key from .env into your script’s environment, and os.getenv() grabs it to set openai.api_key for API calls.
• prompt = "Write a formal...": Adds “formal” to the prompt, telling OpenAI to use a polished, professional tone in the output.
• openai.Completion.create: Sends the prompt to OpenAI’s servers, where text-davinci-003 generates text based on the condition, with max_tokens=50 capping it at ~40-50 words.
• text = response.choices[0].text.strip(): Takes the generated text from the API response, cleaning up extra spaces for a tidy result.

This gets your API and conditioning flowing—next, set advanced parameters.

Step 3: Set Generation Parameters like Temperature and Token Limits

Set generation parameters, including advanced options, to fine-tune your conditioned output with precision.

Coding the Parameters

Update cond_setup.py:

import openai
from dotenv import load_dotenv
import os

# Load API key
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")

# Conditioned prompt with advanced parameters
prompt = "Write a formal explanation of neural networks."
response = openai.Completion.create(
    model="text-davinci-003",
    prompt=prompt,
    max_tokens=50,
    temperature=0.5,
    top_p=0.9,
    frequency_penalty=0.2,
    presence_penalty=0.1,
    n=1,
    stop=None,
    logprobs=None,
    echo=False,
    best_of=1
)

# Display output
text = response.choices[0].text.strip()
print("Conditioned Output with Parameters:")
print(text)

Run python cond_setup.py, and expect:

Conditioned Output with Parameters:
Neural networks are computational models inspired by biological neural systems, comprising interconnected nodes organized in layers. They process input data through weighted connections, enabling pattern recognition and predictive tasks via iterative training.

How It Works

• model="text-davinci-003": Picks text-davinci-003, a robust model trained up to April 2023, great for formal text. Advanced use: Switch to gpt-3.5-turbo for faster, chat-style outputs if formality shifts to dialogue.
• prompt=prompt: Feeds your conditioned text, “formal explanation,” guiding the model’s tone and content naturally.
• max_tokens=50: Limits output to 50 tokens, about 40-50 words, keeping it concise. Advanced use: Set to 200 for a longer, detailed formal report.
• temperature=0.5: Controls randomness—0.5 keeps it focused and formal, avoiding quirky detours. Advanced use: Raise to 0.9 for a less rigid, slightly creative formal tone, like a polished speech.
• top_p=0.9: Chooses from the top 90% probable tokens, adding slight variety while staying on track. Advanced use: Lower to 0.5 for stricter adherence to formal phrasing in legal docs.
• frequency_penalty=0.2: Slightly reduces repetition of words (0-2 range), ensuring variety like “nodes” over “units.” Advanced use: Increase to 1.0 for diverse vocabulary in academic papers.
• presence_penalty=0.1: Encourages new ideas (0-2 range), nudging beyond repetition, like “training” after “systems.” Advanced use: Set to 0.5 for broader concepts in exploratory tech essays.
• n=1: Generates one response. Advanced use: Bump to 3 for multiple formal drafts to pick the best for a manual.
• stop=None: Lets it run to max_tokens without early cutoff. Advanced use: Use ["."] to stop at sentences for short, formal snippets.
• logprobs=None: Skips token probability logs. Advanced use: Set to 5 to analyze top 5 token choices for debugging formality.
• echo=False: Excludes the prompt from output. Advanced use: Set True to log full prompt-output pairs for review.
• best_of=1: Uses one generation. Advanced use: Set to 3, picking the best for a highly polished formal doc.

This tunes your output naturally—next, add logging.

Step 4: Add Logging to Track Outputs

Add logging to keep tabs on your generated outputs and settings.

Coding the Logging

Update cond_setup.py:

import openai
import logging
from dotenv import load_dotenv
import os

# Set up logging
logging.basicConfig(filename="cond_gen.log", level=logging.INFO, format="%(asctime)s - %(message)s")
logger = logging.getLogger()

# Load API key
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")

# Conditioned prompt with parameters
prompt = "Write a formal explanation of neural networks."
response = openai.Completion.create(
    model="text-davinci-003",
    prompt=prompt,
    max_tokens=50,
    temperature=0.5,
    top_p=0.9,
    frequency_penalty=0.2,
    presence_penalty=0.1,
    n=1,
    stop=None,
    logprobs=None,
    echo=False,
    best_of=1
)

# Log and display output
text = response.choices[0].text.strip()
logger.info(f"Prompt: {prompt}")
logger.info(f"Output: {text}")
print("Conditioned Output with Logging:")
print(text)

Run python cond_setup.py, and expect:

Conditioned Output with Logging:
Neural networks are computational models inspired by biological neural systems, comprising interconnected nodes organized in layers. They process input data through weighted connections, enabling pattern recognition and predictive tasks via iterative training.

Check cond_gen.log:

2025-04-10 10:00:00,123 - Prompt: Write a formal explanation of neural networks.
2025-04-10 10:00:00,124 - Output: Neural networks are computational models inspired by biological neural systems...

How It Works

• logging.basicConfig(...): Creates a log file, cond_gen.log, with timestamps and messages, saving what your script does.
• logger = logging.getLogger(): Sets up a logger to write entries, keeping your records neat.
• logger.info(...): Adds the prompt and output to the log, tagging them with the time for easy tracking.
• print(...): Shows the text on screen, while the log keeps a permanent copy.

This keeps a record of your runs—next, verify the setup.

Step 5: Verify Setup with a Sample Conditioned Prompt

Verify your setup by testing a sample conditioned prompt and checking the result.

Coding the Verification

Update cond_setup.py:

import openai
import logging
from dotenv import load_dotenv
import os

# Set up logging
logging.basicConfig(filename="cond_gen.log", level=logging.INFO, format="%(asctime)s - %(message)s")
logger = logging.getLogger()

# Load API key
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")

# Sample conditioned prompt with advanced parameters
prompt = "Provide a formal description of neural networks for a research paper."
response = openai.Completion.create(
    model="text-davinci-003",
    prompt=prompt,
    max_tokens=50,
    temperature=0.5,
    top_p=0.9,
    frequency_penalty=0.2,
    presence_penalty=0.1,
    n=1,
    stop=None,
    logprobs=None,
    echo=False,
    best_of=1
)

# Log and verify output
text = response.choices[0].text.strip()
logger.info(f"Prompt: {prompt}")
logger.info(f"Output: {text}")
print("Verification Output:")
print(text)
print("Check: Formal tone, technical terms, within 50 tokens.")

Run python cond_setup.py, and expect:

Verification Output:
Neural networks are sophisticated computational frameworks modeled on biological neural systems, featuring interconnected nodes in layered structures. They process data through weighted connections, supporting advanced pattern recognition and predictive modeling for research applications.
Check: Formal tone, technical terms, within 50 tokens.

How It Works

• prompt: Uses a formal, research-focused condition to test how well the setup follows instructions.
• response = openai.Completion.create(...): Generates text with all parameters set—model picks text-davinci-003 for reliability, max_tokens=50 keeps it short, temperature=0.5 ensures formality, top_p=0.9 balances variety, frequency_penalty=0.2 avoids repeats, presence_penalty=0.1 adds fresh ideas, n=1 gives one output, stop=None runs to the limit, logprobs=None skips probability logs, echo=False omits the prompt, and best_of=1 uses a single generation. Advanced use cases (e.g., n=3, best_of=5) could refine this for multiple polished drafts.
• logger.info(...): Logs the prompt and text with timestamps, storing them in cond_gen.log for reference.
• print(...): Displays the output with a note to check tone, terms, and length, confirming it meets expectations.

This proves your setup works naturally—you’re all set!

Next Steps: Expanding Your Conditional Setup

Your conditional setup is running and verified! Try conditions like “casual” or pair it with Conditional Text Generation. You’ve got this dialed in, so keep tweaking and creating!

FAQ: Common Questions About Conditional Generation Setup

1. Can I use other conditions?

Yes, “casual” or “brief” work fine—any clear instruction shapes the output.

2. Why use logging?

It saves prompts and results, making it easy to review or fix issues later.

3. What if the tone isn’t right?

Lower temperature or tweak the prompt, like “very formal,” for better fit.

4. How do parameters affect output?

They control length, style, and variety—see OpenAI Docs for more.

5. Can I skip some parameters?

Sure, defaults like top_p=1.0 kick in if unset—your choice.

6. Why verify the setup?

It ensures conditions stick before you scale up.

Your questions are covered—generate with confidence!