1. No Custom Tooling or Workflows

What this means

The product just sends user prompts to OpenAI and returns the text—without any orchestration layer, toolchain integration, or workflow logic.

Why it's limiting

Without custom pipelines (e.g. tool chaining, function calls, retrieval steps), you're offering nothing beyond OpenAI’s standard chat interface. Pure wrappers lack domain-specific value or extensibility.

2. No Grounding Data or Retrieval-Augmented Generation (RAG)

What’s missing

No external data, embeddings, or document retrieval is used—just blind LLM output.

Why it's an issue

Without grounding your responses in sources or real-time data, your product is prone to hallucination, context drift, and stale outputs. RAG isn’t just optional—it’s foundational for accuracy in domain-specific LLM systems.
As recent RAG surveys affirm, retrieval-augmented systems substantially outperform prompt-only setups in reliability and factuality Artificial Intelligence in Plain English.

3. No Error Handling or Output Sanity Checks

What happens

If the model responds with misuse, hallucination, or malformed output, the system fails or displays garbage to users.

Why it matters

Production-grade systems require fallback paths, output validation, and robust error handling. Without schema checking or rejection thresholds (e.g., hallucination detection), wrapper apps expose downstream users to risk and inconsistency.

4. No Value Beyond ChatGPT’s Native Output

Symptom

Try replicating the UI experience using GPT (or another LLM) directly—you get almost identical behavior and output.

Why this indicates a wrapper

If the core value is prompt-engineered responses with minimal customization or product logic, you’re offering little differentiation. As critics note, some startups are charging customers for workflows that can be replicated for a few dollars via direct API use Medium.

5. Poor or Minimal UI/UX Flow

Definition

A simple prompt text box, a logo, and that’s it—no thoughtful interaction, feedback loops, or multi-step UX.

Why that fails

Effective LLM-based products often require guided flows, context management, session handling, or user feedback loops. A prompt box UI alone is a classic wrapper trait, without meaningful product design.

6. No Memory or Session Awareness

What’s broken

Every call is stateless. The system does not store previous interactions, session history, or context continuity.

Why it's limiting

AI products that lack memory can’t support conversational user flows, personalization, or agentic behavior. True “AI product” experiences manage state: context windows, memory storage, historical behavior—wrappers don’t.

7. Only a Prompt Box and a Logo

What this means

A landing page with “Ask AI” button, minimal branding, no integrations or backend logic.

Why it's unsurprising

It's the minimal viable wrapper—very common in AI-washing startups. Former Apple Siri engineers warn that many startups merely rebrand ChatGPT, bypassing privacy, data validation, and product discipline LinkedIn.

How to Know When You’ve Outgrown a Wrapper

Real AI products differentiate by layering:

• RAG pipelines: embedding + retrieval integrated with LLMs

• Tool orchestration or function calling: custom logic and APIs behind the scenes

• Session state & memory: context-aware behavior that evolves per-user

• Error handling, fallback logic, and validation: ensuring reliability

• Domain-specific workflows: not just generic chat responses

Competitive models like Windsurf or Perplexity go beyond wrappers by managing memory, embeddings, agentic flows, and robust UX flows Toloka.

Quick Comparison Table

Final Reflection

Just wrapping an LLM API in a prompt box and slapping on branding is not a product—it’s API reselling. Users and investors increasingly recognize signs of AI washing: value claims without substance. As Nitro’s CTO warns, many products today are surface-deep wrappers with no real problem-solving ability Toloka.

Move toward structured AI orchestration, ground answers in data, build session-aware interactions, handle errors, and design workflows—not just prompts. That transforms a cheap wrapper into a lasting AI product.