gensai.aiA DLLM applies diffusion techniques from image generation to text: instead of producing words one at a time, the model starts from a noisy sequence and refines it globally over several steps. The expected result: potentially fewer hallucinations thanks to iterative refinement, parallelisable and therefore faster generation, and finer control over the output.
In practice at Gensai
Gensai follows this generation of models closely, promising for uses that demand speed, precision and frugality.
Should you use it already?
For now, mainly to experiment. Models such as Mercury, from Inception Labs, or Gemini Diffusion, from Google, show very high generation speeds, especially on code. In production, a classic LLM remains the safest choice, but this technology is worth following for use cases where speed matters.
Common mistake
Taking promises for facts. The announced reduction in hallucinations and gains in accuracy still need to be confirmed at scale: comparing on your own use cases is better than relying on demos.
Same theme
Definition written with AI assistance, checked by Sébastien Lecocq, co-founder of Gensai.
Going beyond the definition?
From concept to project: Gensai builds custom, sovereign, GDPR-compliant AI solutions.
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