From Feature Engineering to Prompt Engineering

Introduction (engineer’s question)
If you’ve ever asked yourself “How do I optimise my telemetry to enable better AI results?” you’re asking about more than just data collection.

In the machine learning (ML) world, this is called feature engineering: transforming raw numbers into structured, meaningful inputs that models can learn from. In the Generative AI world, the equivalent practice is prompt engineering: shaping the inputs so that the AI generates accurate, actionable outputs.

At NetMinded, our MNOC Scoring methodology bridges these two disciplines. It turns raw, unstructured telemetry into enriched data products that improve machine learning and provide clearer signals for AI-driven automation.

Why Raw Telemetry Falls Short

Raw metrics like latency, CPU usage, or WiFi interference don’t tell the full story. Without context, ML models waste effort rediscovering patterns that engineers already know. AI assistants, meanwhile, struggle to make sense of unstructured signals.

MNOC Scoring as Feature Engineering for ML

Feature engineering gives ML models “shortcuts” by shaping raw data into features such as:

• Summaries: averages, trends, deltas over time.
  
  

• Signals: unusual spikes, sustained changes.

• Domain knowledge: known “risk states” encoded as scores.

MNOC Scoring enriches telemetry with these signals. For example, a Red/Amber/Green score can become a categorical feature, while a 0–100 health score becomes a numeric one. These context-rich features make training faster, cheaper, and more accurate.

MNOC Scoring as a Foundation for Prompt Engineering (AI)

Once telemetry is enriched, it becomes useful not just for ML, but also for Generative AI that relies on prompts. For example:

• Instead of prompting an LLM with raw latency values, we can feed it “MNOC score = 25/100, trending down, amber risk state”.
  
  

• Prompts built on MNOC scores let LLMs and AI agents generate actionable responses: “Investigate WiFi interference on channel 40 — high likelihood of customer experience issue.”

In other words, MNOC turns raw telemetry into the building blocks for better prompts.

From Telemetry to MNOC Data Products

This dual role — supporting both ML feature engineering and LLM prompt engineering — transforms telemetry into MNOC Data Products: consistent, shareable signals that work across monitoring, automation, and AI use cases.

Benefits for Engineers

• Data Engineers: cleaner, structured features that reduce prep time.

• DevOps & SREs: enriched signals that improve observability and automation.
  
  

• AI/ML Engineers: faster training and clearer prompts that improve outcomes.

FAQ: Optimising Telemetry with MNOC Scoring

Q1: What is MNOC Scoring?
It’s a methodology that applies domain knowledge to raw telemetry, turning it into structured scores and risk indicators for ML and AI.

Q2: How does it improve ML?
By feeding models structured features instead of only raw numbers, training becomes faster and more accurate.

Q3: How does it support AI/prompt engineering?
Enriched scores form the basis of better prompts — giving LLMs structured, contextual inputs.

Q4: Can it work with WiFi or network telemetry?
Yes — noisy signals like WiFi strength, interference, or channel use can be scored and enriched, making them usable for both ML models and AI assistants.

Q5: Do I need new tools?
No. MNOC Scoring layers on top of existing telemetry pipelines (Influx, Prometheus, Grafana, gNMI collectors, etc.), adding scoring and enrichment without replacing your stack.

Conclusion

The objective is to optimise telemetry for better AI predictive results. 

You start with MNOC Scoring. It transforms raw telemetry into structured features for ML and enriched prompts for AI, bridging the gap between data collection and trusted intelligence.

At NetMinded, we’re making telemetry AI-ready — one scored data product at a time.