GLOSSARY
Observability
Observability helps investigate AI workflows. Identify evidence about errors, delays and cost while avoiding unnecessary logs of confidential customer content.
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AI, sales and negotiation clearly explained. A starting point for better questions, informed decisions and your next practical application.
GLOSSARY
Observability helps investigate AI workflows. Identify evidence about errors, delays and cost while avoiding unnecessary logs of confidential customer content.
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Open-source AI: understand the OSI definition and assess the actual rights, operating responsibilities and switching options behind an openness claim.
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Open model weights provide access to learned parameters. Explore the options they create and the licensing, quality and operating responsibilities that remain.
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Orchestration connects AI, tools and approvals into a workflow. In sales, the quality of handovers determines whether it reduces effort or creates new failures.
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A policy engine evaluates permissions and approvals. It makes sales rules testable, while the application must enforce decisions where each action occurs.
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Probabilistic AI uses probabilities. Understand variable answers, the limits of repeated agreement and the evidence needed for a responsible sales decision.
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A useful prompt specifies the task, context and requirements. Sales teams can improve drafts while keeping assumptions and unanswered questions visible.
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Prompt injection redirects AI through inputs or external content. Assess the risk in sales research and why limited permissions matter beyond prompt wording.
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Quantization can reduce AI model memory requirements. Business decisions need evidence about output quality, target hardware and the cost of accepted results.
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Reasoning models process complex tasks through connected steps. Assess when extra processing improves negotiation options and what evidence is needed.
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A red team deliberately tests weaknesses in AI applications. Clear scenarios, independent assessment and retesting turn findings into evidence of improvement.
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Retention defines which information stays stored and for how long. Sales workflows need a clear purpose, appropriate limits and verified deletion processes.
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RAG adds retrieved information to AI responses. In sales, reliable answers depend on search quality, current documents, permissions and support for each claim.
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ROI for AI projects connects financial benefit with cost. Understand why saved time is not automatically cash saved and how to establish a credible comparison.
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AI scalability handles more users and work at an agreed service quality. Capacity, approval bottlenecks and cost per useful result need joint assessment.
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Self-hosting AI brings responsibility for infrastructure and applications. Assess control, operating capability and full cost for the intended sales workflow.
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Semantic fidelity preserves statements, conditions and uncertainty in AI summaries. It keeps customer interest, assumptions and commitments distinguishable.
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Structured AI outputs supply defined fields for CRM workflows. Valid formats can contain incorrect values, requiring schema checks and substantive review.
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Synthetic data creates targeted AI test cases. Assess how well it represents real work and whether it could reveal information about underlying records.
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Temperature influences output selection in supported AI models. Low settings do not ensure correctness; test configurations against actual task requirements.
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Tenant isolation protects customer boundaries in shared AI applications. Controls must cover storage, search, background work and operational processes.
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Throughput measures work processed over time. Sales teams also need to understand waiting times, output quality and the treatment of urgent requests.
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Tokens are the processing units of language models. They affect capacity and cost; their price alone cannot establish the cost of a useful business result.
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Token budgets control the resources available to an AI request. Useful limits preserve essential information and make incomplete answers visible to the user.
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