Cognosa solutions v. Custom GPTs
One reason we created the Cognosa platform was that while some customers had quick success using Custom GPTs in OpenAI’s ecosystem, over time they found that the no-code approach could not answer certain queries reliably, could not scale to support larger or more complex datasets, or became very expensive at higher usage levels.
This post compares OpenAI’s GPT Builder — how it works, what it does well, and where it differs from a purpose-built Retrieval-Augmented Generation (RAG) solution like Cognosa.
Setting the stage: Custom GPTs vs. a purpose-built RAG solution
Many organizations experimenting with AI start by testing Custom GPTs inside the OpenAI ecosystem. It’s an appealing option: upload some files, add instructions, and instantly get a conversational assistant that seems aware of your documents.
But the simplicity of a Custom GPT comes with inherent limitations. It offers convenience, not full control.
A purpose-built RAG solution — like the one Cognosa provides — takes a different approach. Your data, structure, semantics, indexing strategy, and retrieval logic become first-class components, giving you transparency, customization, and scalability that a generic builder cannot match.
Before jumping to the side-by-side comparison, it helps to understand what Custom GPTs actually do — and just as important, what they don’t.
What a Custom GPT Does Provide
Same base model, customized behavior
A Custom GPT uses the same underlying LLM that powers ChatGPT. You supply instructions and files, but you do not retrain or fine-tune the model’s weights.
Knowledge file uploads
You can upload PDFs, spreadsheets, or text files so the GPT can reference them during a conversation — e.g., “Use these spec sheets when answering product questions.”
Custom instructions
You define how the GPT should behave, how it should use its knowledge files, and what persona or style it should adopt.
Optional tools, actions, and APIs
You can enable external API calls, web browsing, or code execution to extend the assistant’s capabilities.
Internal retrieval from uploaded files
At runtime, the GPT can draw from your uploaded files for additional context. They serve as an internal reference set, not as training data.
No use of your data for public training
OpenAI states that Custom GPTs are not used to train or improve OpenAI’s public models
What a Custom GPT does not (or at least not clearly) do
It is not fine-tuning.
Uploading files does not train new model weights. The GPT uses your files as additional context, not as learned parameters.
It does not replace a full RAG architecture.
You cannot control chunking, embeddings, vector indexes, or retrieval pipelines. The internal mechanics aren’t exposed and may not resemble a traditional RAG pipeline.
It still inherits normal LLM limitations.
Hallucinations, context-window limits, and confusion between similar content can still occur without careful structuring.
It does not permanently embed your data
The GPT uses the base model’s tokenizer and inference engine. Your “knowledge files” act as temporary reference material, not integrated training data.
RAG vs Custom GPT — how they compare
Below is a side-by-side summary of how Cognosa’s RAG-first approach compares with OpenAI’s Custom GPT builder.
| Solution Area | Cognosa / RAG Approach | Custom GPT (OpenAI) |
|---|---|---|
| Data ingestion & indexing | Purpose-built vector database, tuned chunking, overlaps, similarity thresholds, and retrieval pipelines designed for your data and query patterns. | Upload files within limits; generic ingestion and chunking with minimal visibility or control. |
| Granular control over retrieval | Full control of embeddings, vector size, retrieval type, chunk size, and parameters. Tuned during customer validation. | Only files + instructions; embedding and retrieval logic remain opaque. |
| LLM usage | Use open-source or commercial LLMs; deploy in our cloud, your cloud, or on-prem. Open-source avoids per-token fees. | Tied to OpenAI’s API pricing; enterprise costs can be significant. |
| Model fine-tuning | Optional fine-tuning of open-source models; or use smaller models when retrieval matters more than reasoning. | No fine-tuning; behavior controlled only through prompts and context. |
| Latency & maintenance | Performance based on chosen infra; small 24×7 workloads run for low hundreds/month. | Minimal ops overhead; OpenAI manages everything. |
| Traceability & control | Transparent retrieval with logs, auditing, and explicit control over data access. | Opaque retrieval; limited insight into contributing content. |
| Setup speed | RAG is complex to build alone, but Cognosa provides a turnkey managed setup. | Very fast: upload files + instructions and begin testing. |
| Operational cost | Predictable: setup + monthly cost based on hosting, model, and usage. | Ongoing OpenAI token charges; low barrier to start, expensive at scale. |
Next time, we’ll take an advanced look at some of how this magic works: the actual UI that gives advanced users access to alternative retrieval strategies and parameters!