Forget Prompt Engineering

The Real Bottleneck Isn't Prompting — It's Context

An AI model is like a brilliant, Harvard-educated intern who has spent the last two years locked in a featureless room.

They've read the entire internet up to a cutoff date. But they know nothing about:

• the subject property
• the tenant mix
• the site's physical utility
• the real lease terms
• the comps you verified yesterday
• the submarket nuance you learned from interviews

They cannot generate insight unless you hand them the right dossier.

The power isn't in the question.
It's in the context.

Welcome to the Context Window Economy

Every AI model has a context window—a limit on how much information it can hold in working memory at one time.

If you fill that window with:

• unstructured Word documents
• outdated boilerplate
• gigantic PDFs
• broken tables
• irrelevant city data
• inconsistent formatting

…you're polluting the model's reasoning space.

The intern now has to sift through garbage to find the rent roll.

When the AI can't locate a fact in the material you provided, it will often fabricate one to remain helpful. This is what gets labeled as "hallucination"—and in valuation, it is exactly what you do not want.

Structure Is Safety

This is where Context Engineering enters.

Context engineering is the discipline of curating, validating, and structuring information before it ever reaches the model. It turns AI from a guesser into a narrator.

Consider two approaches to drafting a market analysis.

Scenario A: The Prompt Engineer

You:

• paste a 40-page broker PDF into ChatGPT
• design a 12-step prompt demanding it ignore the fluff
• beg it to extract meaningful vacancy data
• hope it doesn't confuse asking rent with effective rent

Outcome: A gamble.

Scenario B: The Context Engineer

You:

• supply only verified data
• structure it into clean fields (rent, vacancy, absorption)
• feed it into the context window
• give a simple instruction: "Write a summary based on these numbers."

Outcome: A repeatable workflow.

What matters is not the spell you cast.
It's the clarity of the material you provide.

The Appraiser as Data Architect

This shift reframes the role of the workfile.

Historically, the workfile was where data went to die—buried in emails, PDFs, and narrative reports. It served its compliance purpose (and still must), but it was never designed for computational use.

In the context engineering era, the workfile becomes something else:

A structured dataset the model can reason from under appraiser control.

If your comparable sales include discrete, validated fields—latitude, longitude, GBA, NOI, cap rate—you can inject those directly into the model's context window.

You are giving the AI:

• verified inputs
• bounded facts
• labeled fields
• a framework it cannot deviate from

The AI stops behaving like a creative writer (dangerous in valuation) and starts behaving like a factual narrator.

Prompts Are Not a Moat. Context Is.

Here's the uncomfortable truth:

Prompts are not intellectual property.

The moment you share a clever prompt, everyone can use it. There is no durable advantage in phrasing good questions.

But if you have:

• ten years of verified commercial comps
• structured operating expense histories
• normalized rent rolls
• submarket trends captured in consistent fields
• and the ability to inject this context into any modern AI model

…then you have something no competitor can replicate.

They can use the same model.
They cannot replicate your context.

In an era where models are commoditized, the advantage is structured, verified reality.

The Future of Appraisal

The future of appraisal is not about learning to talk to robots.

It is about organizing your market reality so the robots can understand it.

Prompt engineering is a passing phase.
Context engineering is the durable skill.

And clean, structured, verifiable data remains the only advantage that compounds over time.