Beyond Standard Metrics: Towards Bespoke LLM Evaluation
Keeping Up in the Age of AI Acceleration
It feels like we’re all navigating a period of incredibly rapid change in the world of Generative AI. New models arrive frequently, each seemingly more capable than the last, shifting the leaderboards almost weekly. We see models like GPT-4o, Claude 3.7 Sonnet, Llama 3, and Gemini 2.5 pro performing impressively on benchmarks like MMLU, HellaSwag, and HumanEval. These metrics tell us important things about their general knowledge, reasoning abilities, and coding prowess.
But when it comes to integrating these powerful tools into specific business processes — say, within a bank, telco, or insurance company — those benchmark scores often don’t tell the whole story.
The questions we find ourselves asking are more grounded:
- “Will this model adhere to our specific compliance guidelines when summarizing sensitive customer interactions?”
- “Can it accurately troubleshoot our product line using our internal knowledge base?”
- “Does the generated marketing copy align with our established brand voice, or will it require heavy editing?”
- “Is the performance gain from the latest `model-X` significant enough to justify the cost increase over `model-Y` for our specific summarization task?”
- “Did our recent prompt engineering efforts actually improve the quality of responses in the areas we care about?”
Standard benchmarks provide a valuable baseline, like knowing a general measure of intelligence. But they don’t fully capture whether a model is the right fit for a specific job, especially when nuanced quality, safety, or adherence to specific rules are paramount.
The Observability Gap: “How Does It Work for Us?”
This gap between general capability and specific applicability is a common challenge. We need effective ways to evaluate LLMs not just on broad knowledge, but on their performance within the context of our unique tasks and according to our specific criteria. We need evaluation methods that reflect the business outcomes and quality standards that matter to our organizations, going beyond generic academic metrics.
While metrics like ROUGE, BLEU, or general RAGAS scores can offer some quantitative signals, they might not capture critical aspects like adherence to regulatory constraints, consistency with internal data sources, or maintaining a carefully crafted brand tone.
Building Our Own Observation Post: Introducing Panopticon 🔭
Facing this challenge ourselves, we started thinking about a more flexible approach. What if we could build a system designed for this kind of tailored evaluation? A framework where you could define your own test queries, establish your own criteria for success, and track how different models perform against your standards over time?
That’s the idea behind Panopticon, an open-source, microservices-based system we’ve developed. It aims to provide a practical foundation for the targeted LLM evaluation and monitoring that specific, real-world use cases require.
How Panopticon Helps Bridge the Gap
Panopticon offers a structured way to move beyond off-the-shelf benchmarks and create evaluation pipelines tailored to your operational reality:
- Define Your Specific Test Cases (Custom Queries as `items`): Instead of relying solely on generic prompts, you store your real-world questions, tasks, or input data snippets as `items` within Panopticon’s `item-storage-queries` service. You categorize these using a `type` field (e.g., a `theme` like “Loan Application QA” or “VDSL Troubleshooting”). We leverage `pgvector` and `sentence-transformers` so you can even search for semantically similar queries later.
- Define Your Success Criteria (Custom `judge prompts` as `items`): This is where you encode what “good” looks like for you. You write evaluation prompts — instructions for a separate “judge” LLM (not the one you are evaluating) — detailing how to assess a response based on your specific needs. These are stored as `items` in the `item-storage-metrics` service. Examples:
— “Review the generated summary against the original document. Does it accurately represent all key regulations mentioned? Score 1–10.”
— “Assess the tone of this customer support response. Does it meet our ‘friendly but professional’ standard? Score 1–10.”
— “Check if the response correctly follows compliance rule X.Y.Z. Score 1 for non-compliant, 10 for fully compliant.
— The `judge-service` then uses these prompts to guide the judge LLM in scoring the output from the model you are testing. - Run Consistent Comparisons (Model Agnostic): Panopticon’s `model-registry` service manages different LLM providers and models, using adapters (like LiteLLM or specific provider adapters) to offer a consistent interface. This allows you to run the exact same queries and evaluation criteria against various models for a fair comparison on the tasks relevant to you.
- Track Performance Over Time: Evaluation shouldn’t be static. As underlying models are updated by providers, or as you refine your own system prompts, you can re-run evaluations through Panopticon. The `judge-service` stores results (including scores, model IDs, themes, and timestamps) in a central database, allowing you to track performance trends and understand the impact of changes.
- Visualize the Insights: Raw scores and data tables can be overwhelming. Panopticon includes a Grafana instant with a dashboard that presents summary statistics, performance timelines, model comparisons (bar/radar charts), and theme-based heatmaps. This helps translate the evaluation data into actionable insights for both technical and business teams.
A Practical Example Workflow
Let’s reconsider the bank scenario evaluating LLMs for summarizing regulatory documents:
Store Test Documents: `POST` your sample regulatory text snippets as `items` to `/api/queries`, each with `type: “Regulatory Summarisation”`.
Store Evaluation Criteria (Judge Prompts): `POST` your judge prompts to `/api/metrics`.
Accuracy Prompt: `item: “Compare summary to original. Accuracy score 1–10?”`, `type: “reg_summary_accuracy”`.
Conciseness Prompt: `item: “Is summary concise without losing key info? Score 1–10?”`, `type: “reg_summary_conciseness”`.
Run the Evaluation: `POST` to `/api/judge/evaluate/theme` to evaluate all queries for the “Regulatory Summarisation” theme using, say, `gpt-4o`, evaluated by `gpt-4` based on your stored metrics.
Analyse the Results: Access the Panopticon Grafana dashboard at `http://localhost:3000`. Here you can find Summary Dashboards, Model Comparisons, Theme Analysis, and filterable tables of individual evaluation records.
Why This Approach Matters
Generic benchmarks offer a wide-angle view of model capabilities. However, deploying LLMs effectively and responsibly in a business setting often requires a closer look — measuring performance on the specific tasks they will perform, against the specific quality and safety criteria that are critical to your operations.
Systems like Panopticon aim to provide that necessary layer of custom observability. It’s about shifting the focus from “Is this model generally capable?” to “Can we trust this model to reliably and safely handle our specific needs?”
Join the Effort
Panopticon is shared as an open-source project because we believe developing robust, context-aware evaluation practices is crucial for the successful adoption of AI in many domains. We encourage you to explore the repository, try it out for your own use cases, and consider contributing your insights or improvements.
The journey to production with Generative AI involves more than just selecting the model with the highest benchmark score. It requires thoughtful, tailored evaluation and continuous monitoring. Let’s work together to build the tools and practices needed to navigate this effectively.
