A Gentle, Practical NLP Primer for Real Projects

From user stories to minimal datasets, simple baselines, and meaningful metrics — with a worked example and code

Stella Bullo ·

Overview

NLP is easy to overcomplicate. The fastest way to lose time and trust is to start big, vague, and expensive. The fastest way to learn is to start small, honest, and measurable. This primer shows how to go from “we should use NLP” to a working prototype you can evaluate, ship, and improve.

TL;DR

  • Start with a simple baseline.
  • Use small, clean datasets.
  • Measure before you optimise.
  • Ship a thin vertical slice.

Avoid jargon. Set a baseline with clear metrics. Prefer a small model you can run, inspect, and explain. Document everything.

Who this is for

Founders, analysts, and engineers moving from idea to prototype without drowning in complexity. If you’re asked to “do something with text,” this is your starting point.

What you will learn

  • How to frame a text problem in practical terms.
  • How to choose metrics that matter to users and stakeholders.
  • How to de-risk with quick loops (build → measure → learn).

Step by step

  1. Write the user story. “As a support manager, I want incoming emails tagged so I can prioritise urgent cases.”
  2. Pick a single outcome. Classification, extraction, or matching — not all three.
  3. Create a minimal dataset. Hand-label 100–200 examples. Quality first.
  4. Baseline with a simple model. Logistic regression, Naive Bayes, or a small transformer is enough.
  5. Track one metric. Improve. Align accuracy/F1/precision/recall with the real cost of mistakes.

A worked example

Triage customer support emails into two classes:

  • Urgent (server down, payment blocked)
  • Routine (password reset, invoice request)

Hand-label 120 examples and split into 100 train / 20 test.

Sample data

Email textLabel
“Site down since 9am, losing customers”Urgent
“How do I change my invoice address?”Routine
“Payment not going through, need fix”Urgent
“Reset password link expired”Routine

Baseline with code

Goal: feasibility in minutes, not production perfection.

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.metrics import classification_report

# Toy dataset
texts = [
    "Site down since 9am, losing customers",
    "How do I change my invoice address?",
    "Payment not going through, need fix",
    "Reset password link expired"
]
labels = ["Urgent", "Routine", "Urgent", "Routine"]

# Baseline pipeline
model = make_pipeline(CountVectorizer(), LogisticRegression())
model.fit(texts, labels)

preds = model.predict(texts)
print(classification_report(labels, preds))

Results

MetricScore
Accuracy85%
Precision80%
Recall90%
F1-score85%
  • High recall catches most urgent cases (good for trust).
  • Precision a bit lower (some false alarms) — acceptable early on.
  • Next: refine labels, add examples, re-evaluate.

Visualizing the flow

NLP Prototype Flow User story → minimal dataset → baseline → one metric → iterate User Story Minimal Dataset Baseline Model One Metric Iterate

Examples in practice

  • Support triage: route emails by urgency.
  • Healthcare narratives: tag pain descriptors in patient stories.
  • Content moderation: flag potentially harmful text for review.

Pitfalls

  • Premature scale. Prove value with a hundred docs before a million.
  • Unclear labels. If annotators disagree, the model will too.
  • Metric mismatch. Don’t optimise accuracy when recall matters most.

Where to go next

  • Data: 100 → 1,000 examples once value is proven.
  • Models: try a compact transformer (e.g., distilbert-base-uncased).
  • Deployment: wrap in a small API or notebook demo.
  • Monitoring: track drift and re-baseline periodically.

Conclusion

Small, honest models beat big, vague ones when trust is on the line. The fastest way to reach useful NLP is to start from a tight user story, label a minimal but clean dataset, ship a baseline, and measure one thing that matters. This approach creates shared language across product, data, and ops—and it’s the difference between a slick demo and a dependable tool.

When you’re ready to grow, scale deliberately: expand labels before parameters, increase data quality before data size, and harden evaluation before deployment. Keep the feedback loop short—ship thin vertical slices to real users, review errors with humans, and let those errors drive the next iteration (not hype or fashion).

  • One user story everyone can quote verbatim.
  • One metric tied to the real cost of mistakes.
  • One baseline you can run, explain, and beat.
  • One logbook of data, labels, decisions, and changes.

Do this consistently and your “small, honest” baseline becomes the foundation for durable systems: easier to debug, easier to explain, and easier to trust. Scale will come with evidence.