The ECHO FRAMEWORK
The ECHO FRAMEWORK
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    • Home
    • ECHO
      • AI prompting: overview
      • The ECHO Framework
      • ECHO Basic
      • ECHO Plus
      • Persistent ECHO
      • Dynamic ECHO
    • Audit gaps
      • AI assurance: overview
      • Input manipulation risks
      • Disclosure risks
      • Supply chain risks
      • Free diagnostic review
    • Human-led AI
      • Human-led AI: overview
      • ISO 9001
      • Other audit gaps
      • Risk controls
      • Sandbox environments
    • Training
      • Training: overview
      • Gradual upskilling
      • Confidentiality
    • About
      • Eye For Business
  • Home
  • ECHO
    • AI prompting: overview
    • The ECHO Framework
    • ECHO Basic
    • ECHO Plus
    • Persistent ECHO
    • Dynamic ECHO
  • Audit gaps
    • AI assurance: overview
    • Input manipulation risks
    • Disclosure risks
    • Supply chain risks
    • Free diagnostic review
  • Human-led AI
    • Human-led AI: overview
    • ISO 9001
    • Other audit gaps
    • Risk controls
    • Sandbox environments
  • Training
    • Training: overview
    • Gradual upskilling
    • Confidentiality
  • About
    • Eye For Business

AI PROMPTING FOR AMBITIOUS ENTERPRISES

WHY STRUCTURE MATTERS

DOWNSIDES OF AD-HOC PROMPTING

DOWNSIDES OF AD-HOC PROMPTING

Structured prompting imposes discipline—delivering consistent, audit-ready outputs with process documentation and control over quality. Multi-model optimisation enhances results further. 

DOWNSIDES OF AD-HOC PROMPTING

DOWNSIDES OF AD-HOC PROMPTING

DOWNSIDES OF AD-HOC PROMPTING

Unstructured, trial-and-error prompting leads to inconsistent answers, scalability limitations, increased risk and inefficient resource utilisation. 

ECHO: EVIDENCE-BASED AI

DOWNSIDES OF AD-HOC PROMPTING

ECHO: EVIDENCE-BASED AI

A four-part structure (E.C.H.O.) addresses gaps in enterprise AI—each pillar is practical, documented and builds internal capability. 

CORE COMPONENTS FOR DECISION-GRADE OUTPUTS

EXPERTISE (E)

Expertise prompts frame the AI’s role as an informed analyst, defining the professional context and expected standards. This creates a structured, expert dialogue—ensuring domain accuracy, meaningful business language, and responses that align with stakeholder needs. 

CONTEXT (C)

Context gives the AI background, business constraints, regulatory scope and strategic aims—ensuring outputs are grounded in operational reality and support compliance from the outset. 

HOW (H)

How provides a logical route to solve the problem—encouraging multi-step, transparent reasoning that decomposes tasks and imposes quality checks at every stage. 

OUTPUT (O)

Output clarifies the required deliverable, ensuring consistency, traceable metadata, audit readiness and compliance documentation as standard. 

WHAT YOU GET

PERFORMANCE GAINS

Greater accuracy, consistency, regulatory proof and cost-efficiency across teams. 

RISK CONTROLS

Built-in oversight, audit trails, and template-driven processes reduce errors and support appropriate challenge. 

BEST-PRACTICE DESIGN

Multi-model checks, domain-specific language and stepwise improvement underpin robust, audience-relevant results every time. 

RESEARCH-BACKED AI DESIGN PRINCIPLES

MODEL VALIDATION

CONTINUOUS IMPROVEMENT

CONTINUOUS IMPROVEMENT

Using multiple AI models improves reliability, enabling cross-verification and reducing the risk of hidden errors

CONTINUOUS IMPROVEMENT

CONTINUOUS IMPROVEMENT

CONTINUOUS IMPROVEMENT

Structured feedback and iteration drive steady gains in output quality, transparency and operational efficiency 

DOMAIN LANGUAGE

CONTINUOUS IMPROVEMENT

AUDIENCE ALIGNMENT

Industry-specific terms and standards increase answer relevance and ensure outputs reflect industry requirements 

AUDIENCE ALIGNMENT

AUDIENCE ALIGNMENT

AUDIENCE ALIGNMENT

Tailoring communication matches outputs to user roles and decision contexts for greater business value 

QUALITY ASSURANCE

AUDIENCE ALIGNMENT

CONSISTENT FORMATS

Embedded verification steps detect and correct errors, minimising hallucinations and assuring accuracy 

CONSISTENT FORMATS

AUDIENCE ALIGNMENT

CONSISTENT FORMATS

Templates maintain uniformity, streamline workflows and meet compliance or documentation needs across use cases 

CONTACT US

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The ECHO Framework™

Prompt engineering by Eye For Business (Eye4B)

  • Home
  • AI prompting: overview
  • The ECHO Framework
  • AI assurance: overview
  • Human-led AI: overview
  • Training: overview
  • Eye For Business

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