You're overthinking it. Most eval frameworks are overkill for a 5-person team. You don't need a complex scoring system, you need to know if the outputs are correct and safe for finance.
Pick one metric that matters and automate a check. For a finance tool, that's factual accuracy against your data. Use a simple script.
* **Forget RAGAS, LangSmith, TruLens.** They add complexity you can't support.
* **Build a basic test suite.** Compare LLM outputs to your known-correct data.
* **Use a cheap model (gpt-3.5-turbo, claude-haiku) as a judge.** Ask it if the answer matches the source. It's good enough.
```python
# Pseudo-code for a pragmatic check
def evaluate_factuality(llm_answer, source_text):
prompt = f"""
Answer: {llm_answer}
Source: {source_text}
Question: Does the Answer directly and correctly reflect the Source? Reply only 'YES' or 'NO'.
"""
judge_response = call_cheap_llm(prompt)
return judge_response.strip() == "YES"
```
Add checks for forbidden financial advice. That's your framework. Keep it simple.
Simplicity is the ultimate sophistication
I'm a solo dev at a 7-person fintech startup. I handle our whole infra, and we run a RAG pipeline in production for financial document Q&A. I ended up building our own eval loop after testing a few tools.
**Deployment time vs. control**: LangSmith's cloud took an afternoon to integrate with our existing LangChain code. But it added latency - about 200-300ms extra per chain step in my tests. Our own script added no latency.
**Real cost for a small team**: Most frameworks charge per "trace" or "run". LangSmith would've been ~$200/month for our volume. TruLens looked cheaper until I saw the separate hosting cost for their feedback functions. A DIY script using GPT-3.5-turbo as judge costs us under $10/month in extra OpenAI usage.
**Fit for finance-specific checks**: None of the major frameworks have built-in metrics for factual accuracy against private data, which is what we needed. I had to write custom feedback functions anyway, which defeated the purpose. They're better for tracking LLM call quality and latency.
**Hidden operational load**: Even a hosted service requires you to maintain instrumentation in your code. When we changed a prompt, we had to update test datasets in LangSmith. Our simple script lives in the same repo and updates with the code.
I'd recommend sticking with the basic script for now. It's free, immediate, and you own it. If you start needing to track complex chains or compare multiple prompt versions, then look at LangSmith. For a clean call, tell us how many unique prompts you're running and if your team is already using LangChain/LlamaIndex.
Agree on keeping it simple. The cheap judge model works, but I'd test its reliability on your specific finance data first.
I ran a similar check and got false positives when numbers in financial statements were close but not exact. The judge would sometimes say "YES" for a 4.7% vs 5.2% difference because the context was similar. That's a critical failure for finance.
Add a secondary rule-based check for exact numeric extraction alongside the LLM judge.
Exactly the kind of pitfall I'd worry about. A generic LLM judge lacks the precision for financial numbers.
We hit a similar issue with percentage changes in quarterly reports. The judge model would call "mostly correct" if the narrative was right, even if the delta was wrong. That's a hard no for compliance.
Adding a rule-based check for numbers is smart, but it can get messy parsing varied formats (like "4.7%" vs "four point seven percent"). I'd start with a simple regex to catch obvious numeric mismatches first, *then* run the LLM judge for reasoning and context. It filters out the clear failures cheaply before spending tokens.