Building an LLM suitability evaluator gives your team a repeatable way to decide when a large language model actually helps and when it creates hidden costs. I will walk you through a small Python CLI that sends a task description to Oxlo.ai and returns a structured pros and cons analysis. You can drop this into internal tooling or CI pipelines to sanity-check AI proposals before writing any prompts.

What you'll need

• Python 3.10 or newer
• An Oxlo.ai API key from https://portal.oxlo.ai
• The OpenAI SDK: pip install openai

Step 1: Scaffold the project and configure the Oxlo.ai client

Create a file named llm_evaluator.py. We only need the standard library and the OpenAI SDK. Point the client at Oxlo.ai's base URL and pick a model that follows system instructions reliably. I use llama-3.3-70b because it is a strong general-purpose flagship on Oxlo.ai with no cold starts.

import json
import sys

from openai import OpenAI

client = OpenAI(
 base_url="https://api.oxlo.ai/v1",
 api_key="YOUR_OXLO_API_KEY", # replace with your key from https://portal.oxlo.ai
)

MODEL = "llama-3.3-70b"

Step 2: Lock down the system prompt

The system prompt does all the heavy lifting. It forces the model to act as a skeptical engineering advisor and return strictly JSON. This removes parsing headaches and keeps the analysis concise.

SYSTEM_PROMPT = '''
You are a pragmatic engineering advisor. A user will describe a business task they are considering automating with an LLM.

Analyze the task and return a single JSON object with these exact keys:
- "task_summary": a one-sentence summary of the task.
- "advantages": an array of 2 to 4 specific advantages of using an LLM for this task.
- "disadvantages": an array of 2 to 4 specific disadvantages or risks.
- "recommended_approach": either "use_llm", "use_llm_with_human_review", or "use_traditional_software".
- "confidence": either "low", "medium", or "high".

Be specific. Avoid generic statements like "LLMs are powerful." Focus on cost, latency, accuracy, and maintenance.
'''

Step 3: Build the evaluator function

This function wraps the API call. We enable JSON mode so the model is constrained to valid output, then parse the result into a native Python dictionary.

def evaluate_task(task_description: str) -> dict:
 response = client.chat.completions.create(
 model=MODEL,
 messages=[
 {"role": "system", "content": SYSTEM_PROMPT},
 {"role": "user", "content": task_description},
 ],
 response_format={"type": "json_object"},
 )

 raw = response.choices[0].message.content
 return json.loads(raw)

Step 4: Add the CLI wrapper

I want to run this from the terminal against arbitrary task descriptions. A simple main block reads the argument, calls the evaluator, and prints a readable report.

if __name__ == "__main__":
 if len(sys.argv) < 2:
 print("Usage: python llm_evaluator.py 'Describe the task here'")
 sys.exit(1)

 task = sys.argv[1]
 result = evaluate_task(task)

 print(f"Task: {result['task_summary']}")
 print(f"Confidence: {result['confidence']}")
 print(f"Recommendation: {result['recommended_approach']}")
 print("\nAdvantages:")
 for adv in result["advantages"]:
 print(f" - {adv}")
 print("\nDisadvantages:")
 for dis in result["disadvantages"]:
 print(f" - {dis}")

Run it

Here is a real invocation evaluating whether to use an LLM for automated customer refund triage. Because Oxlo.ai charges a flat rate per request, pasting a long policy document as the task description does not inflate the cost.

$ python llm_evaluator.py "Automate tier-1 customer support refund requests by reading the user's order history and deciding whether to approve, deny, or escalate based on company policy."

Task: Automate tier-1 refund decisions using order history and policy rules.
Confidence: medium
Recommendation: use_llm_with_human_review

Advantages:
 - Reduces average handle time for repetitive refund inquiries.
 - Can parse unstructured customer messages and map them to policy clauses.
 - Scales instantly during high-traffic periods without hiring temporary staff.

Disadvantages:
 - Financial risk if the model misinterprets policy edge cases.
 - Requires frequent retraining or prompt updates when policies change.
 - Potential compliance issues if decision logs are not auditable.

Wrap-up and next steps

You now have a working evaluator that turns vague AI ideas into structured risk assessments. A practical next step is to batch-process a CSV of proposed features by looping over rows and appending the JSON output. If you need deeper reasoning for highly technical tasks, swap the model to kimi-k2.6 or deepseek-v3.2 on Oxlo.ai without changing any client code. The flat per-request pricing means you can feed the system long requirement specs or multi-turn conversation histories for analysis and still pay the same single-request cost, which is useful when evaluating complex agentic workflows. Check https://oxlo.ai/pricing to see how the tiers map to your volume.