LLMs work best when the user defines their acceptance criteria first
Source: Dev.to
Why Acceptance Criteria Matter for LLM Projects
Ever had that moment when you’re about to dive into a new tech project and think, “What the heck am I even trying to achieve here?” I’ve been there. While exploring large language models (LLMs), one thing keeps popping up: they work best when you clearly define your acceptance criteria first. It’s like trying to navigate a city without a map—sure, you might get somewhere, but it likely won’t be where you intended.
The GPS Analogy
Defining what success looks like for an AI project can dramatically affect its outcome. Teams that have a clear vision of their success metrics from day one often see their projects “click,” while others go off the rails. Think of acceptance criteria as a GPS for your project; without it, you’re just wandering around.
A Personal Story: From Chaos to Clarity
I once jumped into a generative‑AI project without setting clear goals. My aim was to create a chatbot that could hold realistic conversations. After countless hours, I realized the bot could produce witty one‑liners but often veered into nonsensical territory.
Only after I sat down and defined specific criteria—e.g., maintaining context for at least three exchanges—did the project start to show real promise.
A Detailed Example
- Goal: Build a customer‑support chatbot using OpenAI’s GPT‑3.
- Acceptance Criteria:
- Accurately address ≥ 80 % of user queries.
- Maintain a friendly tone.
- Resolve queries without straying off‑topic.
With these criteria in place, I used a combination of few‑shot prompting and reinforcement learning from human feedback (RLHF). By continually testing against the criteria, I could iteratively improve performance.
# Sample function to interact with the OpenAI API
from openai import OpenAI
def get_response(prompt: str) -> str:
response = OpenAI.Completion.create(
engine="text-davinci-003",
prompt=prompt,
max_tokens=150,
n=1,
stop=None,
temperature=0.7,
)
return response.choices[0].text.strip()
# Example usage
user_query = "How can I track my order?"
bot_response = get_response(user_query)
print(f"Bot: {bot_response}")Each time the bot failed to meet a criterion, I tweaked the prompts or added more training data. It was a grind, but the satisfaction of finally nailing those responses was worth it.
Balancing Flexibility and Rigor
Your criteria should be flexible but grounded in reality. I’ve seen teams become so rigid that they end up in a nit‑picking contest rather than a productive workflow. Striking the right balance is critical.
- Treat acceptance criteria as a living document that evolves with your project.
- Let the criteria grow as the model learns—much like a child learning to walk.
Edge‑Case Awareness
In a sentiment‑analysis project, I set a high accuracy bar but ignored nuanced language (sarcasm, irony). The model couldn’t differentiate between sarcasm and genuine praise, leading to disappointing results. The lesson? Acceptance criteria must consider edge cases.
Practical Tips for Drafting Acceptance Criteria
- Write them before you write any code.
- Use visual tools (e.g., Figma, Miro) to map them out.
- Loop in your team—diverse perspectives enrich the criteria.
- Include ethical considerations (fairness, transparency) especially for LLMs.
Looking Ahead
LLMs are on the brink of becoming even more contextual and adaptive. With great power comes great responsibility—defining acceptance criteria that encompass fairness and transparency will be crucial.
Let’s Connect
If you enjoyed this article, let’s keep the conversation going!
- LinkedIn: Connect with me on LinkedIn
- GitHub: Check out my projects on GitHub
- YouTube: Master DSA with me! – Data Structures & Algorithms tutorials 🚀
- Portfolio: Visit my portfolio
LeetCode Practice
I solve daily LeetCode problems and share solutions on my GitHub repository, covering:
- Blind 75 problems
- NeetCode 150 problems
- Striver’s 450 questions
If you also solve daily LeetCode problems, feel free to contribute! If you’re stuck on a problem, let’s collaborate.
🚀 Let’s Learn and Grow Together!
Feel free to check out my solutions and join the journey.
LeetCode Solutions
LeetCode Profile
📚 Love Reading?
If you’re a fan of books, I’ve written a fantasy‑fiction series you might enjoy:
The Manas Saga: Mysteries of the Ancients – an epic trilogy that blends Indian mythology with modern adventure.
- Immortal warriors, ancient secrets, and a quest that spans millennia.
- Follows Manas, a young man whose destiny is tied to the Mahabharata. He sets out to restore the sacred Saraswati River and confront dark forces threatening the world.
You can find the series on Amazon Kindle (also available with Kindle Unlimited):
Buy on Amazon Kindle
Thanks for reading! Feel free to reach out if you have any questions or want to discuss tech, books, or anything in between.