We often assume that any domain-specific NLP system needs a full RAG stack — embeddings, vector DBs, retrievers, indexing, the whole pipeline.
But in several real-world projects I’ve reviewed, including a Commercial Real Estate (CRE) sentiment engine (details anonymized), the surprising truth is:
A single LLM with structured-output constraints can replace the entire RAG pipeline.
Let’s break down how — and why — this works.
🧩 1. The original task (CRE sentiment engine)
The system needed to:
interpret CRE terminology
classify market sentiment
extract signals like leasing, investment appetite, development activity
produce confidence scores
process thousands of documents
generate region-based heat-map data
It looked like a classic NLP challenge requiring:
dataset cleaning
embeddings
fine-tuning
RAG
a vector DB
a batch-processing pipeline
But none of that was actually required.
🟩 2. The simplified architecture (LLM → JSON → aggregation)
If you define:
1️⃣ A clear JSON schema
2️⃣ Explicit domain instructions
3️⃣ Strict structured-output constraints
Then the LLM can perform:
semantic understanding
sentiment scoring
signal extraction
confidence estimation
domain reasoning
All in one pass.
No embeddings.
No vector DB.
No retrievers.
No fine-tuning.
Just a structured task.
🛠️ 3. Example JSON Schema
Here’s a simplified version of what the LLM outputs:
{
"sentiment": 0.42,
"confidence": 0.87,
"signals": {
"leasing": "cooling",
"investment": "neutral",
"development": "softening"
},
"region": "Germany",
"city": "Berlin",
"period": "2025-Q1"
}
his is immediately usable for:
dashboards
BI tools
heat maps
time-based analytics
scoring pipelines
No post-processing nightmares.
🔁 4. Batch-processing thousands of texts
A simple Python loop is enough:
for text in records:
payload = prompt_template.format(text=text)
response = llm(payload)
save_to_db(response)
No retrievers.
No indexing.
No additional infrastructure.
🗺️ 5. Regional aggregation (also without RAG)
Once you have structured output, you can aggregate by:
country
region
city
sector (office, retail, industrial, etc.)
quarter / month
A LLM can even help generate aggregated summaries, but you can also do it manually in Python.
The key is:
structure → aggregation becomes trivial.
🧠 6. Why does this work?
Because modern LLMs excel at:
interpreting domain language
following schemas
extracting structured meaning
generating consistent classifications
When you frame the task as:
“Read this, then return structured JSON in this exact format.”
You unlock the LLM’s best behavior while avoiding its weaknesses.
🧨 7. What this means for developers
RAG is powerful, but it is also:
costly
complex
brittle
noisy
overused
For many domain sentiment tasks, RAG adds overhead without adding value.
A JSON-first LLM pipeline is:
faster
cheaper
easier to maintain
surprisingly accurate
production-friendly
And yes — it’s good enough for enterprise workloads.
🧑💻 Final thoughts
Before defaulting to RAG, ask:
“Can a structured-output LLM solve 90% of this problem?”
In many cases, the answer is yes.
This changes the economics of NLP development — dramatically.
🙌 Want the templates?
If anyone wants:
the JSON schema
the prompt pack
the batch-processing example
the aggregation template
Let me know — happy to share.
✍️ About the author
I’m Yuer, an independent AGI systems architect.
I write about practical, controllable, production-grade LLM engineering.
Follow me for more real-world AI engineering patterns.
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