Use Ruthless for explanations, drafts and useful ideas

Choose a task with a result you can judge

“Abliterated” describes a model modified to reduce refusal behaviour. Research has investigated how refusal can be changed, but it does not establish that such a model tells the truth more reliably. [1] You still need a clear task and a way to judge the output. For a beginner, a useful first job is improving a message whose meaning you already understand.

Visit getruthless.io, sign in if needed and open chat. In Choose model, inspect the available standard and abliterated options and select a model suited to your task. Paste your request into Message Ruthless and press Send. Start with a short, non-sensitive example. The model does not know your files, business rules or personal history unless you provide the relevant context.

Turn a rough note into a clear message

Suppose you run a pottery class and need to move one session. Your safe input is: “Thursday class moves from 6 to 7, same studio, tell people sorry and ask them reply if cannot come.” State the reader and tone so the model does not invent a refund policy or a different venue.

A good answer keeps Thursday, 7 pm and the same studio, gives a brief apology and asks anyone unable to attend to reply. Check those facts yourself. If it adds “Everyone receives a free class”, ask for a correction: that offer was never in your source. Fluent wording is useful only when it preserves your meaning.

Rewrite this pottery-class notice for existing customers in a warm, direct tone, under 70 words. Facts: Thursday’s class moves from 6 pm to 7 pm; the studio stays the same; apologise briefly; ask people to reply if they cannot attend. Do not add an explanation, refund, offer or guarantee. After the draft, list the factual details you preserved so I can check them.

Use an error as a learning conversation

Debugging means finding why something behaves differently from what you expected. You can begin without knowing code. Say what you clicked, what you expected and what actually appeared. For example: “In my own recipe app I clicked Save after entering a title. I expected a new recipe; the screen said ‘Title required’.” This gives the model a concrete mismatch to reason about.

Ask: “Explain three possible causes in ordinary language and the smallest observation that distinguishes them. Do not edit anything yet.” A useful answer might ask whether the title still appears in the box after clicking Save. It should call causes possibilities, not claim it inspected your project. If technical evidence is needed, ask your builder for the relevant error with keys, addresses and personal details removed.

Explore ideas without turning guesses into research

For planning a local workshop, provide constraints: “Six adults, a £20 materials budget already set, a quiet room and one hour.” Ask Ruthless for three activity ideas, why each fits and what you would need to verify. The result is idea generation. It is not proof that supplies are currently available at a stated price.

A useful answer might propose paper collage and say to verify material costs and accessibility needs. If you need current opening times or evidence about a health claim, find an appropriate current source and bring the relevant excerpt. Ask the model to distinguish what the source says from its own suggestions.

Your exercise is to run the pottery prompt, then request a version half as long. Compare the two for lost facts. This teaches a reusable habit: change one instruction, inspect what changed and keep your own judgement over the result. More text is not automatically more help.