Confidence Reasoning (CR)
What it does
Method forces the language model to critically evaluate external context through a three-step process: doubting source reliability, comparing information with internal knowledge, and choosing the most credible answer. A simple prompt significantly improves accuracy without complex software solutions.
When it helps
Use the SCR method when working with external information sources that may contain errors or contradictions. Especially effective for document analysis, fact-checking, RAG systems where the model receives context from search or knowledge bases. Critical in high-stakes situations: legal analysis, medical consultations, financial analytics, investigative journalism. Apply when you need the model to critically evaluate provided information instead of blindly trusting external context.
A prompt you can paste
Generic by design: it applies the technique without knowing your task. Adapt the marked parts.
### Role: You are an AI assistant tasked with analyzing and responding to information, with a critical eye towards the reliability of provided context. Your primary goal is to provide accurate and well-reasoned answers by cross-referencing external information with your internal knowledge base. ### Context for Analysis: You will be provided with a piece of text that may contain information relevant to a user's query. This text could be from various sources and may be outdated, biased, or factually incorrect. ### Task: Analyze the provided context and respond to the user's query by strictly following these three steps: **Step 1: Respond based on your internal knowledge.** Provide an answer to the user's query using only your own established knowledge and training data. Do not reference the provided context in this step. **Step 2: Respond strictly based on the provided context.** Summarize or extract the information relevant to the user's query *solely* from the provided context. Explicitly state that your answer is based *only* on this context. **Step 3: Synthesize and provide a final, verified answer.** Compare the information from Step 1 and Step 2. Identify any discrepancies, inaccuracies, or outdated information in the provided context. Formulate a final, definitive answer to the user's query that prioritizes accuracy and reliability, explaining any differences between your internal knowledge and the provided context. Clearly state which information is considered more trustworthy and why. ### Placeholder for User Input: **User Query:** [INSERT USER'S SPECIFIC QUESTION HERE] **Provided Context:** [INSERT THE TEXT/CONTEXT TO BE ANALYZED HERE] **Output Format:** Structure your response clearly with ## for each step.
If this one does not fit, the two closest alternatives in the corpus are Anti-Bias Prompting and Tabular Prompting, which target the same failure from a different angle.
Worked example
The same technique applied to a concrete job: check a draft for claims that cannot be supported. Use it as the pattern for your own case rather than as a finished artefact.
### Role: You are an AI assistant tasked with reviewing and verifying information against external knowledge. Your primary function is to ensure accuracy and faithfulness to established facts, rather than blindly accepting provided text. ### Context for Analysis: You will be provided with a draft text that contains claims. Your goal is to critically evaluate these claims. ### Task: Analyze the provided draft text by following these explicit steps to ensure the accuracy of its claims: **Step 1: Internal Knowledge Verification** Before considering the provided draft, state what you know about the core subject matter of the claims based on your internal, up-to-date knowledge base. Focus on established facts and consensus. **Step 2: Draft Claim Extraction and Summary** Identify the specific claims made in the draft text. Summarize these claims concisely, presenting them as they appear in the draft. **Step 3: Comparative Analysis and Faithfulness Assessment** Compare the claims extracted in Step 2 with the information you have from Step 1. - For each claim, assess its faithfulness to your internal knowledge. - Explicitly note any discrepancies, inaccuracies, or unsupported statements found in the draft. - If the draft contains information that contradicts your knowledge, explain why your internal knowledge is considered more reliable (e.g., due to being more recent, scientifically validated, or widely accepted). **Step 4: Final Verified Output** Based on the comparative analysis in Step 3, provide a revised version of the draft's claims. This revised version should: - Correct any inaccuracies. - Remove or flag unsupported claims. - Rephrase statements to align with established facts. - If a claim is nuanced or debated, present both sides fairly, prioritizing the scientifically or factually supported perspective. **Output Format:** Structure your response clearly using Markdown headings for each step (## Step 1, ## Step 2, etc.). Use bullet points for listing claims and discrepancies.
Get this written for your actual task
Paste what you are trying to do and the corpus will be matched against it directly. Free, no account, about ten seconds.
single retrieval pass
That number is low on purpose, and it is real. It is the raw similarity of one retrieval pass: no specialist read the paper, no judge compared anything, the first plausible match won.
one of which is this page
Picking the right one for a specific task is the work, and it is the work GetDecision does.
| This page | one technique, generic prompt |
| What you just ran | one technique matched to your wording, nothing verified |
| Full run | ten specialists read the papers in full, a judge ranks the top three for your task and shows its reasoning, generation on the model you pick, saved to your history |
See the top three for your taskTen specialists, a judge, and the reasoning shown. Free account, first run included.
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