Learn by using GetRuthless
Keep this guide beside your Ruthless chat. Copy one prompt, add the example it asks for and paste it into “Message Ruthless”. Nothing is sent until you choose to send it.
The short answer
A useful GetRuthless evaluation measures whether candidate models solve your recurring engineering tasks within your cost and review budget. Build a small reproducible fixture set, define acceptance before generation and keep unseen failures separate from prompt tuning.
Sample tasks from the work you actually do
Start with six to ten compact cases across your normal workload: explaining a module, repairing a state bug, changing a parser and identifying an account-boundary mistake. Use sanitised snapshots with dependency versions and the commands needed to reproduce them. Remove credentials and real customer data. Keep enough surrounding code to make the correct change possible.
For a parser fixture, include the public contract, representative valid inputs, the failing edge case and a harness that observes behaviour. Do not encode a preferred implementation unless the architecture truly requires it. A different correct patch should pass; a patch that merely edits a snapshot or disables a check should not.
Define acceptance and separate held-out failures
For each case, write the required behaviour, compatibility constraints and permissible scope. A useful rule might require preserving existing valid input handling while rejecting one malformed timestamp. Add a runtime check of both behaviours and review the final diff for unrelated changes. Readability and review effort can be scored separately from functional correctness.
Reserve several failures that you do not use to adjust the prompt. Otherwise each improvement may simply teach the evaluation answers. Keep a record of prompt revisions and identify which cases influenced them. When you change the repository snapshot, distinguish a new evaluation version from a rerun of the old one.
Compare controlled requests and actual work
Choose current model IDs from the public catalogue. You could compare claude-sonnet, gpt-terra and kimi-pro-abliterated on the same fixtures, then add ruthless-forge if a specific question remains. These examples are candidate choices, not a performance claim. Hold input context and task instructions constant; record effort, speed, output allowance and tool availability. [1][3]
If one candidate has repository tools and another only receives snippets, you are comparing complete systems, not isolating model quality. That may be appropriate, but label it. Evaluate the actual generated patch with the fixture harness, then inspect whether the patch undermines the test or introduces a new dependency. Separate model failure, harness failure and unavailable infrastructure.
Record time to a usable patch, attempts needed, human review effort and confirmed account charges. Tokens alone omit correction loops and possible extra tool or web usage. Website chat and API share the Ruthless balance; check current pricing before allocating a comparison budget. This guide does not report a free benchmark or any paid model tests. [1][2]
Make the caller own the evaluation budget
Decide the maximum number of requests, context size, response allowance and permitted external operations in the harness before execution. Your evaluation runner should stop creating new work when its authorised experiment budget is exhausted and retain completed results. These are controls for your own experiment, not new restrictions on GetRuthless customers.
Review failure clusters rather than only an average score. A model that saves time on explanations but repeatedly misses account boundaries may fit one role and fail another. Re-run a small unchanged control set when prompts or client tooling change. Adopt the smallest model-and-workflow combination that meets your acceptance checks, with its limitations visible.
An exercise you can prepare without spending anything: turn three recently fixed bugs into redacted fixtures and write their observable acceptance criteria. Ask a colleague to solve one from the supplied context. If they cannot identify the expected result, improve the fixture before evaluating models.
Help design an evaluation from these redacted engineering tasks. For each, define required behaviour, scope constraints, a runnable check and an unrelated behaviour that must remain intact. Identify missing context and reserve held-out cases. Do not choose a winning model or invent measurements. Tasks: [sanitised cases].Common questions
How many fixtures are enough?
Enough to represent your current decision, not every possible task. Start small, inspect failure patterns and add cases when a meaningful risk is missing.
Can I rank models using one successful patch?
That establishes one observed success under one setup. Repeat across representative and held-out cases before making a broader routing decision.
Sources & further reading
The example prompts and workflows are GetRuthless guidance. Source references support the concepts explained; they are not an endorsement of GetRuthless.
Put the guide into practice.
Open Ruthless and choose an available standard or abliterated model suited to your task. Copy a prompt above, add the context it asks for and check the result. You can compare models in the same workspace; see current pricing before sending requests.