How Adeloop Uses RAG — and Why Structured Data Requires Execution, Not Retrieval
RAG is powerful.
But if you're using RAG for CSV files and SQL tables, you're building a fragile system.
Retrieval-Augmented Generation was designed for semantic retrieval over unstructured content. It was never designed to replace deterministic computation over structured datasets.
At Adeloop, we use RAG where it is scientifically correct — and we deliberately avoid it where it breaks down.
That architectural decision changes everything.
Where RAG Actually Works: Unstructured Data
Unstructured data includes:
- PDF documents
- Web pages
- Text-heavy reports
- Images and diagrams
These data sources share two essential characteristics:
- They do not have a fixed schema.
- Their meaning is embedded in natural language or visual context.
You cannot run SQL on a paragraph.
You cannot aggregate a diagram.
To answer questions about this kind of content, you need semantic retrieval. That is exactly what RAG (Retrieval-Augmented Generation) is built for.
How Adeloop Uses RAG for Unstructured Data
For unstructured data, Adeloop uses a classic but carefully engineered RAG pipeline:
1. Ingestion
- PDFs are parsed into structured text chunks
- Images are converted into semantic representations
- Web content is indexed into meaningful text blocks
2. Embedding
- Each chunk is transformed into vector embeddings
3. Retrieval
- Relevant chunks are selected using similarity search
4. Context Injection
- Retrieved content is injected into the model context
- The AI reasons over grounded information instead of guessing
This enables questions like:
- “What does this contract say about penalties?”
- “Summarize this technical report.”
- “Compare these two policy documents.”
In these scenarios, RAG is not optional — it is the correct architectural tool.
Why RAG Fails on Structured Data
Now consider structured data:
- CSV files
- SQL tables
- Financial metrics
- Time series
The problem here is not retrieval.
The problem is computation.
When you embed structured rows as text, you destroy:
- Numeric precision
- Aggregation logic
- Constraints and relationships between columns
Vector similarity search is inherently fuzzy.
Structured analytics requires determinism.
If a user asks:
“What was total revenue in Q4 grouped by region?”
A similarity search cannot compute a sum.
It cannot enforce grouping.
It cannot guarantee correctness.
At best, it approximates.
At worst, it hallucinates.
For real analytics systems, approximation is unacceptable.
The Execution-Based Approach in Adeloop
For structured data, Adeloop uses execution-based reasoning instead of retrieval-based reasoning.
The workflow looks like this:
- The user asks a question in natural language.
- The AI generates SQL or Python code.
- The code runs inside a secure sandbox.
- The system observes the real computed results.
- The AI explains verified outputs.
The model is no longer predicting answers from text embeddings.
It is executing logic against the real dataset.
This preserves:
- Mathematical accuracy
- Logical consistency
- Deterministic aggregation
- Data integrity
The AI does not just sound correct.
It becomes computationally correct.
The Hybrid Architecture
Adeloop combines two paradigms:
- Unstructured data → RAG
- Structured data → Code execution
This mirrors how humans work:
- We read documents to understand meaning.
- We run queries to compute facts.
Forcing one method to solve both problems creates unstable systems.
Separating retrieval from execution creates reliable ones.
The Core Insight
RAG is a retrieval mechanism.
It is not a reasoning engine.
True reasoning over structured data requires:
- Execution
- Observation
- Iteration
That principle defines how Adeloop is built.
Final Thought
The real question is not:
“Can RAG solve everything?”
The real question is:
“What is the correct computational tool for this data type?”
When you answer that correctly, you stop building AI demos — and start building reliable AI systems.
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