Just built a simple scoring system for tool evaluations. Feedback welcome.

benchmark_nerd_1337 · 2026-07-30T18:00:59Z

I've been increasingly frustrated with the subjective, "vibe-based" evaluations of AI tools, particularly when it comes to retrieval-augmented generation (RAG) pipelines and agentic workflows. The discourse often lacks the rigor we apply to benchmarking databases or cloud instances. To that end, I've prototyped a simple, reproducible scoring system for tool evaluations and I'm seeking constructive feedback from this community. The core premise is that any tool (e.g., a PDF parser, a web search API, a code interpreter) can be evaluated across four quantifiable dimensions, each weighted according to the evaluator's specific use-case priorities. The system outputs a single, comparable score, but—crucially—retains the component breakdown for analysis. **The Proposed Scoring Dimensions:** * **Accuracy/Precision (A):** Measured as a percentage of correct outputs on a standardized task suite. For a parser, this could be text extraction fidelity; for a code tool, execution success rate. * **Latency (L):** P95 latency in milliseconds for a standardized operation. This is transformed into a score via a normalized decay function (e.g., a target of 1000ms, with penalties increasing non-linearly beyond that). * **Cost Efficiency (C):** Calculated as `(cost per successful operation * 1000)`. Lower is better, normalized against a baseline. This captures both raw cost and the accuracy component. * **Throughput Stability (T):** The coefficient of variation (standard deviation/mean) for operations-per-second under a sustained load. Lower variation scores higher. **The Implementation (Prototype):** Each dimension is scored from 0.0 to 1.0. A weighted geometric mean is used for the final aggregate to penalize severe weakness in any one area. The configuration is defined in a simple YAML, and the runner is a Python script. ```yaml # evaluation_config.yaml tool: "Unstructured.io Parser" test_suite: "pdf_suite_v1" weights: accuracy: 0.4 latency: 0.3 cost: 0.2 throughput_stability: 0.1 baselines: latency_ms: 1000 cost_per_1k_ops: 5.00 ``` ```python # scoring logic snippet def calculate_composite_score(dimension_scores, weights): # Geometric mean to emphasize balanced performance product = 1.0 for dim, weight in weights.items(): product *= (dimension_scores.get(dim, 0) ** weight) return product # Example output for a hypothetical tool Tool: "Docling Parser v2.1" Dimension Scores: Accuracy: 0.92 (92% on extraction suite) Latency: 0.75 (P95=1450ms vs 1000ms target) Cost Efficiency: 0.88 ($4.10 per 1k successful ops) Throughput Stability: 0.95 (CoV=0.12) Weighted Composite Score: 0.874 ``` I am specifically looking for feedback on the following: * Are these the correct primary dimensions, or are we missing a critical axis (e.g., ease of integration, which could be proxied by "time to first successful call")? * The choice of geometric mean over arithmetic mean for the composite. The intent is to harshly penalize a tool that fails catastrophically in one dimension (e.g., extremely high cost or latency), even if it excels in others. Is this too punitive? * Standardization of test suites. This is the hardest part. For parsers, we could create a shared corpus; for APIs, a mock server. How might we, as a community, begin to curate and version such reference benchmarks? The goal is not to create an absolute ranking, but to force evaluations into a structured, reproducible, and comparable format. I believe this could elevate discussions from "I found tool X nice" to "Tool X scores 0.82 under a high-accuracy weighting, but its latency score drops to 0.4 under load, whereas Tool Y maintains 0.75." numbers don't lie

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cloud_ops_learner_99

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01/08/2026 5:03 am

Nice start! The four dimensions make sense. For the latency decay function, are you including any test for consistency? I get worried about tools with low P95 that still have a high P99 or variance - that's a killer for user-facing systems.

Also, do you factor in cost per successful operation anywhere? That's a huge deal for us when picking APIs. Accuracy is great, but if it's 10x the price, we can't use it.

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