METRICS: Braidr’s prompting framework that makes AI work harder for you

AI should make work faster, smarter and easier. Some teams get structured insights, reproducible analysis and explainable reasoning. Others get guesswork, noise and contradictions.

The difference isn’t creativity. It isn’t expertise. It isn’t even the model. It’s the prompt.

Prompts are now the language layer between humans and machine intelligence. They decide truthfulness, bias, format, reasoning depth and usability. They shape whether AI becomes an accelerator for strategic work or another source of digital clutter.

To solve this, we built METRICS – a structured prompting system we’ve refined across real client workloads. It reduces common failure modes, lowers variance across models and makes the outputs easier to reproduce.

This expanded guide explains how METRICS works and what it unlocks for data, AI and commercial teams.

Why does prompting need a framework?

Prompting has become a core capability inside modern organisations, yet some teams treat it as an informal, ungoverned practice. The result is inconsistent quality and avoidable inefficiency. Across thousands of interactions, we see the same three patterns emerge:

1. They rely on intuition
People write prompts the same way they write emails. This leads to ambiguity and forces models to guess context.

2. They lack standardisation
Every team has its own style, meaning outputs are hard to compare or integrate.

3. They scale poorly
What one analyst knows about prompting never becomes organisational capability.

Without a robust prompting system, teams hit the same failure modes over and over. METRICS reduce the prompt-driven failures, such as unclear tasks, missing context, inconsistent structure and weak reference data. Other model-level issues are best handled with techniques like Retrieval Augmented Generation (RAG), adversarial prompts and validation checks.

What does METRICS help teams do?

METRICS is a seven-part prompting system engineered for precision, clarity and consistency. It provides a scaffold for every stage of interacting with AI, from setting the right persona to producing the final structured output.

It breaks prompt engineering into seven practical steps:

• Model selection: Choosing the right model for your task
• Expert role assignment: Tailoring AI persona to your data needs
• Task definition: Crystal clear instructions with constraints
• Reference data: Strategic data sampling
• Iterative refinement:t Building on previous outputs
• Context size management: Maximal data utilisation
• Structured output: Format control techniques

When applied together, these components improve reproducibility and reduce variability across outputs.

How does METRICS work?

1. Model selection

Each AI model excels at different types of work. Choosing the right model for the task is the first step toward getting accurate, reliable and high-quality output.

2. Expert role assignment

AI produces better work when given a clear identity. Expert roles turn generic models into specialised assistants and help in shaping:

• vocabulary
• reasoning style
• depth
• focus
  quality of interpretation

Example prompt: “Act as a BI analyst specialising in SaaS metrics analysis.”

Tailor the persona to your needs: “Focus on app performance trends and user engagement metrics.”

3. Task definition

Vague tasks force AI to guess. Precise tasks reduce uncertainty. 

Good task definition tells AI:

• what to analyse
• how deeply
• what the goal is
• what is out of scope

Better clarity equals better intelligence.

Example prompt: “Analyse app performance data, focusing on daily active users (DAU), install completion rates, and revenue per download.”

4. Reference data

Models cannot assume your KPIs, data structure or business logic. Reference data gives them a foundation. Examples of useful references:

• sample tables
• JSON structures
• KPI definitions
• benchmark values
• anonymised user data

Good reference data prevents wrong schema assumptions
invented metrics and inconsistent formats. It anchors the model in your real world.

Example prompt: 

“Here’s sample data from our top three apps:

[Insert anonymised dataset as JSON].

Analyse trends across DAU and revenue.”

5. Iterative refinement

The highest-quality results emerge through iteration, not one-shot prompts. Iterative refinement gives you:

• better depth
• cleaner structure
• fewer errors
  more alignment to your intent

AI becomes a collaborator, not a vending machine.

Initial prompt: “Expand analysis to include user retention rates and compare with industry benchmarks.”

6. Context size management

Large datasets overwhelm models; small ones under-inform them. Chunking data month-by-month avoids overwhelming the model while maintaining focus.

This leads to cleaner reasoning and more reliable trend analysis.

Example prompt: “Analyse app performance data for January first, then expand to February after initial insights.”

7. Structured output

Structure turns intelligence into action. Examples:

• bullet insights
• tables
• JSON
• tagged summaries
• KPI blocks
• headline + evidence format

Without structure, outputs become narrative. With structure, outputs become usable. 

Example prompt: “Provide insights in bullet points, followed by a summary table of key metrics.”

Applying METRICS in practice

To show how the framework works end-to-end, here is a real example of METRICS applied to a search performance analysis task. In this scenario, the prompt is designed to guide the model to review paid and organic data and translate the findings into clear actions.

How to fix AI hallucinations?

Even with a strong prompting framework, complex analytical tasks still require safeguards to ensure accuracy. These techniques help validate outputs, stress test reasoning and ground AI responses in verified data so teams can trust the insights they receive.

1. Double-check the numbers

• AI outputs must match real operational data.
• Cross-check results against known values, run deterministic calculations in parallel and use reasoning-enabled models to reveal step-by-step logic.
• This ensures numerical accuracy in BI, forecasting and financial analysis.

2. Test with adversarial prompting

• Stress testing exposes weaknesses before they affect real work.
• Ask contradictory or edge-case questions, rephrase prompts to check consistency and use chain-of-thought reasoning to uncover logic gaps.
• This strengthens reliability and reduces silent errors.

3. Apply Enterprise RAG

• Grounding responses in verified internal data reduces hallucination.
• Retrieve relevant metadata or documents, enforce source attribution and use structured query generation to extract facts directly from databases.
• This aligns outputs with your business logic and audited information.

Final thoughts

AI is only as good as the method guiding it. Without structure, it improvises. With structure, it behaves more predictably.

METRICS, gives teams a shared, scalable way to work with AI that reduces noise, increases clarity and unlocks better decision-making.

Connect with us to shape your next phase of intelligence and turn AI into a true performance driver.