Challenges Working with Vision-Language Models (VLMs)

Despite the exciting capabilities of Vision-Language Models, implementing them in a real-world internship setting surfaced several roadblocks. This page documents those hurdles — both technical and contextual — that I encountered during my journey.

Lack of Learning Resources

Most VLM tutorials focus on text-only models like GPT or BERT.
Resources for multi-modal prompting, image scoring, or image-text reasoning are scarce or fragmented.
No centralized guides on prompt formatting for VLMs (especially for structured outputs like left=0.55, right=0.48).

Output Instability

Submitting the same image multiple times to a VLM can yield inconsistent float scores.
Slight model drift due to non-determinism, temperature sampling, or model updates causes unreliable results.
Lack of OpenAI-style reproducibility guarantees for image inputs (unlike text completions with seed values).

Infrastructure & Tooling Challenges

Open-source VLM alternatives (e.g., BLIP-2, LLaVA) require high-end GPUs, making experimentation inaccessible on basic hardware.
No direct support for prompt version tracking, output caching, or prompt metric benchmarking.
Gradio + OpenAI integrations were limited in image fidelity, causing model hallucination or formatting errors.

Prompting Difficulty

Image-based prompts are difficult to condition like text prompts.
Lack of tooling for CoT (Chain of Thought) or few-shot prompting in image inputs.
It was hard to teach models quantitative scoring (e.g., bulge levels in facial regions) without fine-tuning datasets or ground truth supervision.

Limited Community Support

Forums like Stack Overflow or Hugging Face had few discussions around VLM scoring logic.
Most help is focused on captioning or retrieval, not structured numeric outputs from vision inputs.
Collaborating on prompt reproducibility or visual consistency tests felt like working in isolation.

Trade-offs Between API Use vs Local Models

Criteria	OpenAI API	Open-Source (e.g., LLaVA)
Ease of use	Plug-and-play	Requires setup + GPU
Output stability	Varies by session/prompt ver	Slightly better with fixed seed
Cost	Paid per call	Free if local hardware exists
Prompt customization	Prompt IDs supported	Requires deeper knowledge

Takeaway

“VLMs are powerful but currently under-documented. There’s a huge gap between research demos and deployable, reproducible VLM pipelines. My internship was an exercise in bridging that gap — with limited data, hardware, and literature.”

Suggestions for Future Developers

Document every prompt version and its expected output format
Use image caching to reduce re-submission variability
Build your own float comparison tools to measure numeric output drift
Treat VLMs like humans — they need clear, literal, repeatable instructions