Just spent the last two weeks deep in Freeplay, specifically testing their evaluation system for LLM outputs. I'm coming from a background of stitching together custom eval scripts with Pipedrive data, so my first instinct is always to build my own. But Freeplay's built-in evaluators (factuality, relevance, etc.) are surprisingly robust.
So when does it make sense to use their out-of-the-box stuff versus rolling your own? Here's my breakdown:
**Freeplay's Built-In Evaluators (The Good):**
* **Speed to launch:** You can have a factuality check running on your pipeline in minutes. The pre-configured LLM-as-judge prompts are solid starting points.
* **Maintenance-free:** They handle the judging infrastructure, model calls, and scoring logic. No need to worry about prompt drift or updating your evaluation code.
* **Standardized metrics:** Great for team alignment. Everyone understands what a "7.5 in relevance" means if you're all using the same base evaluator.
**Where I Hit Limits & Switched to Custom:**
* **Domain-specific logic:** I needed to check if a support agent response correctly referenced our internal product codes (a format like "APP-XXX-YYYY"). The built-in "correctness" evaluator couldn't be tuned finely enough for that pattern.
* **Complex workflow gates:** I wanted an evaluator that would only run if a previous step's output contained a specific keyword. Had to build a custom chain to manage that conditional logic.
* **Cost control on judging:** Their built-ins use GPT-4 by default. For high-volume, lower-stakes checks, I wrote a custom evaluator using a cheaper model (like Claude Haiku) to keep costs down.
The hybrid approach seems to be the winner for me right now: using Freeplay's built-ins for the broad-strokes health metrics (is the response generally helpful? is it safe?), and dropping in a few custom evaluators for the business-critical, nuanced checks that are unique to our workflows.
Has anyone else tried mixing them? Curious if you found the built-in evaluators' scoring thresholds adjustable enough, or if you also ended up writing custom ones for more control.
Still looking for the perfect one
I'm a data engineering lead at a mid-market logistics platform where we use Freeplay to evaluate our customer support chatbot and internal document Q&A pipelines. We've been running both built-in evaluators and custom ones in production for about eight months.
* **Development velocity vs. control trade-off**: Using Freeplay's built-in evaluators, we had factuality, relevance, and toxicity checks wired into a new RAG pipeline in under two hours. Writing a custom evaluator for the same coverage took two days of prompt engineering, testing, and orchestration code. The built-ins save 80-90% of the initial setup time.
* **Cost transparency for simple cases**: The built-in evaluators are priced into the platform seat fee (roughly $15-25/user/month at our tier), so there's no per-evaluation surprise. Custom evaluators that use GPT-4 as the judge, however, directly pass through OpenAI's API costs. We budget about $0.30 per 1k evaluations for a custom GPT-4 judge analyzing a 500-token response.
* **Brittleness with highly structured data**: The built-in correctness evaluator failed for our use case of validating invoice numbers (e.g., "INV-2024-10001"). It would accept semantically close but syntactically wrong formats. We had to write a custom evaluator with a regex pattern match and a business logic check against our billing system; the built-in LLM-as-judge approach couldn't guarantee the required 99.9% precision.
* **Maintenance overhead shifts**: Freeplay handles versioning and prompt updates for their built-in evaluators. For our custom evaluators, we own that lifecycle. We've had to update prompts quarterly due to minor model drift and once due to a schema change in our knowledge graph, which added about 5 hours of maintenance per quarter for three custom evals.
If you're validating unstructured text quality (factual alignment, tone, relevance) and need to move fast, use the built-ins. If you're validating against structured data, internal codes, or require deterministic logic, you must go custom. To make a clean call, tell us the required precision for your APP-XXX-YYYY check and whether those codes exist in a system you can query via API.
SQL is not dead.
Your point about the cost transparency is crucial and often overlooked in these discussions. The built-in evaluators essentially turn a variable operational cost into a fixed, predictable overhead. For teams budgeting quarterly, that's a significant accounting advantage.
Your example of "highly structured data" like invoice numbers is the perfect boundary case. The built-in evaluators use general-purpose LLM-as-a-judge prompts. They're fundamentally probabilistic and semantic, which fails when you need deterministic, syntactic validation. For that, you have to drop down a layer.
We hit a similar wall validating API endpoint names in generated code snippets. The built-in "correctness" evaluator would accept a syntactically wrong but semantically plausible endpoint like `/api/v1/users/{id}/profile` instead of the exact spec `/api/v1/user/:id/profile`. The solution was a custom evaluator with a simple regex match, not an LLM call at all. It highlights that "evaluation" isn't a single problem - sometimes it's fuzzy logic, sometimes it's exact string matching, and the platform's built-in tools are only designed for the former.
—KH