Building LLM Evaluation Pipelines That Don’t Burn Your API Budget
Hook
Every time you restart a crashed LLM evaluation run, you’re potentially burning hundreds of dollars in redundant API calls. This lightweight template solves that problem with a file-based caching layer that makes evals interruptible and resumable.
Context
If you’ve ever run large-scale LLM evaluations, you know the pain: a run crashes halfway through, you’ve burned significant API credits, and restarting means either re-running everything or writing custom checkpoint logic. The jplhughes/evals_template repository addresses this exact friction point by providing a structured foundation for evaluation projects that treats API cost and developer time as first-class concerns.
Built around Hydra for configuration management and supporting both OpenAI and Anthropic APIs specifically, this template prioritizes two things most evaluation frameworks overlook: resumability and rate limit optimization. Rather than being a comprehensive benchmarking suite with pre-built metrics, it’s deliberately minimal—a starting scaffold that handles the annoying infrastructure pieces (caching, rate limiting, cost tracking, configuration management) so you can focus on writing evaluation logic specific to your use case. The repository includes MMLU dataset integration as a reference implementation, demonstrating how to structure a complete evaluation pipeline from data loading through result caching. Built with Python 3.11, it requires basic setup with virtual environments and API keys for both providers.
Technical Insight
The architecture centers on a single entry point (evals/run.py) orchestrated by Hydra’s configuration system. Every experiment requires an explicit exp_dir parameter, which becomes the isolated namespace for that run’s cache, logs, and configuration snapshots. This design choice prevents accidental cache collisions and makes experiments fully reproducible—you can examine any past run’s exact Hydra config alongside its results.
The caching mechanism is elegantly simple: before making an API call, the system generates a hash from the prompt content and model parameters, then checks if that hash exists in the experiment’s cache directory (which defaults to $exp_dir/cache). If found, it returns the cached response; if not, it makes the API call and writes the result to disk. This means you can Ctrl+C a run at any point, fix a bug, and restart without wasting a single API credit on prompts you’ve already evaluated. The cache is prompt-specific, not dataset-specific, so if you’re running variations of the same prompt across different experiments, you’ll still benefit from shared caching.
Here’s a practical example of running a simple evaluation with custom parameters:
python3 -m evals.run \
++exp_dir=exp/gpt4_cot_eval \
prompt=cot \
++language_model.model=gpt-4 \
++language_model.temperature=0.7 \
++limit=100 \
++print_prompt_and_response=trueThis command structure reveals the Hydra integration: the ++ prefix indicates runtime overrides, prompt=cot selects a predefined prompt configuration from evals/conf/prompt/, and you can override nested config values like language_model.temperature without touching YAML files. The framework automatically logs the complete configuration to your experiment directory, creating a paper trail for every run.
The rate limit optimization is particularly clever. Most LLM inference wrappers implement exponential backoff when hitting rate limits, which wastes time. This template instead allows you to specify openai_fraction_rate_limit to proactively throttle requests to a fraction of your limit, avoiding rate limit errors entirely through thread count management via anthropic_num_threads and openai_num_threads. Even more interesting: you can pass a list of model names like ["gpt-3.5-turbo", "gpt-3.5-turbo-0613"] to effectively double your rate limit, since OpenAI rate limits are per-model endpoint.
The prompt system uses string templating with variables like $question that get populated at runtime. Prompts are defined as OpenAI-format message arrays in YAML files under evals/conf/prompt/, making it trivial to version control your prompt variations and compose them with different model configurations. For example, you might create evals/conf/prompt/cot.yaml with a chain-of-thought system message, then test it across multiple models by simply changing the language_model config reference.
Beyond inference, the repository includes a complete finetuning workflow in evals/apis/finetuning/ with Weights & Biases integration for tracking training runs. The CLI utility provides helper functions for managing OpenAI’s file storage:
python3 -m evals.apis.finetuning.cli list_all_files --organization FARAI_ORG
python3 -m evals.apis.finetuning.cli delete_all_files --organization FARAI_ORGThis is particularly useful since OpenAI has file storage limits, and old finetuning datasets can accumulate quickly. The finetuning runner accepts parameters like n_epochs and notes, making it easy to script hyperparameter sweeps. The usage tracking modules (evals/apis/usage/) provide visibility into API consumption across your organization, which is critical for budget planning when running large evaluation suites.
Pydantic data models in evals/data_models/ enforce type safety across the codebase, catching configuration errors before you waste API calls. The separation of concerns is clear: evals/apis/inference/ handles all LLM communication, evals/load/ manages dataset processing (with MMLU as the included example), and evals/conf/ contains composable configuration fragments. This modularity makes it straightforward to swap out the MMLU dataset for your own evaluation data—you’d primarily modify the data loading logic while keeping the caching and API management intact.
Gotcha
The biggest limitation is one you’ll hit immediately: this template doesn’t include any evaluation metrics or analysis tools. It handles prompt execution and response caching beautifully, but once you have a directory full of cached LLM responses, you’re on your own for scoring, aggregation, or comparison. There’s no built-in accuracy calculation, no statistical significance testing, no visualization generation. It’s infrastructure, not insights. If you’re expecting something like HELM or OpenAI Evals with pre-built evaluation harnesses and standardized metrics, you’ll be disappointed—this is deliberately lower-level.
The documentation is limited to the README, with no contribution guide, architectural decision records, or troubleshooting section. The repository has just 7 stars and no visible community activity, meaning you’re unlikely to find Stack Overflow answers or GitHub issues documenting edge cases. The MMLU integration is the only concrete example of structuring an evaluation, and there’s no guidance on adapting to custom tasks like code generation, summarization, or conversational agents. You’re essentially getting a well-structured scaffold that you’ll need to extend significantly for real-world use. The finetuning utilities are OpenAI-specific with no Anthropic equivalent, and there’s no apparent support for local models or alternative providers like Cohere or AI21. If you need those capabilities, you’ll be implementing them yourself or looking elsewhere.
Verdict
Use this template if you’re starting a greenfield LLM evaluation project where you need fine-grained control over prompts, caching, and API cost management, and you’re comfortable building your own metrics layer on top. It’s particularly valuable if you’re running systematic prompt experiments with frequent iterations—the caching and Hydra integration will save you significant time and money. Teams doing hyperparameter sweeps across multiple models or conducting A/B tests on prompt variations will appreciate the reproducible configuration management and the ability to pause and resume runs without penalty. Skip it if you need a turnkey evaluation suite with pre-built benchmarks, community support, or immediate metric calculations. The minimal documentation and tiny user base (7 stars) make this better suited as a fork-and-customize starting point rather than a maintained dependency. If you’re in research mode and want standardized comparisons, use HELM or OpenAI Evals instead. If you need production observability with dashboards and team collaboration, Langfuse is more appropriate. This template shines in the narrow use case where you want structured infrastructure without opinionated evaluation logic—but be prepared to write significant custom code to turn cached responses into actionable insights.
