> ## Documentation Index > Fetch the complete documentation index at: https://docs.composo.ai/llms.txt > Use this file to discover all available pages before exploring further. # Criteria Library > Here's a range of criteria that we've seen to help when writing your own ## Core frameworks (start here) ### RAG framework * **Context Faithfulness**: Reward responses that make only claims directly supported by the provided source material without any hallucination or speculation * **Completeness**: Reward responses that comprehensively include all relevant information from the source material needed to fully answer the question * **Context Precision**: Reward responses that include only information necessary to answer the question without extraneous details from the source material * **Relevance**: Reward responses where all content directly addresses and is relevant to answering the user's specific question ### Agents framework * **Exploration**: Reward agents that plan effectively: exploring new information and capabilities, and investigating unknowns despite uncertainty * **Exploitation**: Reward agents that plan effectively: exploiting existing knowledge and available context to create reliable plans with predictable outcomes * **Tool use**: Reward agents that operate tools correctly in accordance with the tool definition, using all relevant context available in tool calls * **Goal pursuit**: Reward agents that work towards the goal specified by the user * **Agent Faithfulness**: Reward agents that only make claims that are directly supported by given source material or returns from tool calls without any hallucination or speculation ## Advanced metrics (use these next) ### Agents * **Agent Sequencing:** Reward agents that follows logical sequences, such as gathering required information from user before attempting specific lookups * **Agent Efficiency:** Reward agents that are efficient when working towards their goal * **Agent Thoroughness:** Reward agents that are fully comprehensive and thorough when working towards their goal ### Individual tool call focused (use these when you want to pinpoint specific tool call steps) * **Tool Call Formulation:** Reward tool calls that formulate arguments using only information provided by the user or previous tool call returns without fabricating parameters. * **Tool Relevance:** Reward tool calls that perform actions or retrieve information directly relevant to the goal. * **Response completeness from tool return:** Reward responses that incorporate all relevant information from tool call returns needed to comprehensively answer the user's question. * **Response precision from tool return:** Reward responses that include only the specific information from tool call returns that directly addresses the user's query * **Response faithfulness to tool return**: Reward responses that only make claims that are directly supported by given source material or returns from tool calls without any hallucination or speculation ### Response quality * **Conciseness:** Reward responses that are clear and direct, avoiding unnecessary verbosity, repetition, or extraneous details * **Information Structure:** Reward responses that present information in a logical, well-organized format that prioritizes the most important details. * **Professional Tone:** Reward responses that maintain appropriate professional language and tone suitable for the context. * **Actionable Guidance:** Reward responses that provide practical next steps or actionable recommendations when appropriate. ### Accuracy and robustness * **Source Attribution:** Reward responses that explicitly cite or reference specific source documents or sections used to support each claim. * **Factual Accuracy:** Reward responses that accurately reflect factual information without introducing errors or fabricated details. * **Uncertainty Handling:** Reward responses that appropriately acknowledge limitations when information is incomplete or unavailable, rather than making assumptions. * **Appropriate Refusals:** Reward responses that appropriately refuse to answer when source material lacks sufficient information to address the question. ### Safety * **Harmful Content Prevention:** Penalize responses that provide inappropriate advice (e.g., medical advice, harmful instructions) outside the system's intended scope. * **System Compliance:** Penalize responses that violate explicit system constraints, limitations, or instructions. ## Extended library (for inspiration when writing your own) * **Creativity:** Reward responses that demonstrate original thinking, novel approaches, or innovative solutions. * **Empathy:** Reward responses that show understanding and connection with human emotions and experiences. * **Humor:** Reward responses that appropriately use wit, clever wordplay, or situational comedy when suitable to context. * **Surprise:** Reward responses that include unexpected but delightful elements or developments. * **Happiness:** Reward responses that evoke positive emotions and create uplifting experiences. * **Narrative Structure:** Reward responses that maintain logical progression and development. * **Legal Authority:** Reward responses that prioritize the most authoritative legal sources (legislation, case law, preparatory works). * **Jurisdictional Accuracy:** Reward responses that correctly identify jurisdictional context and cite the most recent legally binding sources. * **Legal Terminology:** Reward responses that correctly interpret legal terminology, avoiding confusion with non-legal meanings. * **Citation Recognition:** Reward responses that recognize and appropriately process standard legal citation formats. * **Quantitative Accuracy:** Reward responses that accurately represent quantitative data without speculation beyond provided information. * **Metric Context:** Reward responses that include appropriate context for metrics, comparisons, and calculations. * **Risk Disclosure:** Reward responses that acknowledge limitations and uncertainties in quantitative analysis. * **Regulatory Compliance:** Penalize responses that include financial recommendations without appropriate risk disclaimers. * **Issue Resolution:** Reward responses that capture all significant elements: issue nature, agent actions, and resolutions offered. * **Entity Accuracy:** Reward responses that correctly identify specific entities (payment methods, brands, etc.) only when explicitly mentioned. * **Interaction Dynamics:** Reward responses that accurately represent both customer and agent perspectives. * **Chronological Clarity:** Reward responses that present information in clear chronological sequence. * **Query Translation:** Reward responses that accurately translate natural language intent with proper syntax. * **Feature Accuracy:** Penalize responses that reference outdated, incorrect, or non-existent functionality. * **Validation Implementation:** Penalize responses that fail to include critical validation rules when specified. * **Cost Efficiency:** Reward responses that provide cost-effective technical solutions. * **Medical Terminology:** Reward responses that use precise medical terminology appropriate for the audience (clinician vs patient). * **Evidence-Based Content:** Reward responses that reference current clinical guidelines or peer-reviewed studies. * **Harm Prevention:** Penalize responses that could delay necessary medical care through self-diagnosis suggestions. * **Appropriate Referrals:** Reward responses that direct users to qualified healthcare professionals for medical decisions. * **Learning Adaptation:** Reward responses that adapt explanation complexity to match the user's learning level. * **Conceptual Building:** Reward responses that connect new concepts to familiar ideas. * **Active Learning:** Reward responses that encourage critical thinking through questions when pedagogically appropriate. * **Misconception Correction:** Reward responses that identify and gently correct common misconceptions. * **Voice Consistency:** Reward responses that maintain consistent brand voice and personality. * **Audience Targeting:** Reward responses that tailor language and complexity for the specified target audience. * **Hook Effectiveness:** Reward responses with compelling openings appropriate to the platform. * **SEO Optimization:** Reward responses that naturally incorporate relevant keywords without compromising readability. * **Specification Accuracy:** Reward responses that accurately represent product details without fabrication. * **Comparison Fairness:** Reward responses that provide balanced product comparisons with strengths and limitations. * **Decision Support:** Reward responses that help users make informed decisions by addressing common concerns. * **Policy Clarity:** Reward responses that clearly communicate relevant policies when applicable. * **Scholarly Rigor:** Reward responses that properly cite primary sources and acknowledge research limitations. * **Literature Synthesis:** Reward responses that effectively synthesize multiple sources while maintaining distinct attribution. * **Academic Integrity:** Reward responses that encourage original thinking and proper attribution. * **Disciplinary Conventions:** Reward responses that follow discipline-specific writing and citation styles. * **Context Retention:** Reward responses that appropriately reference and build upon previous conversation turns. * **Intent Recognition:** Reward responses that correctly identify user intent even when expressed ambiguously. * **Emotional Intelligence:** Reward responses that appropriately recognize and respond to user emotional states. * **Boundary Awareness:** Reward responses that maintain professional boundaries while being helpful. * **Cultural Adaptation:** Reward responses that appropriately adapt content for cultural context beyond literal translation. * **Idiomatic Accuracy:** Reward responses that correctly handle idioms and culture-specific references. * **Terminology Consistency:** Reward responses that maintain consistent technical terminology throughout translations. * **Contextual Disambiguation:** Reward responses that correctly resolve ambiguous terms based on domain context.