LLMs

One interesting thing about LLMs is that they can actually recover (and without error loops). You can have a step that doesn't work right, and a later step can use its common-sense knowledge to ignore some of the missing results, conflicting information, etc. One of the problems with developing with LLMs is that the machine will often cover up... See more

Ensuring availability during peak traffic by maintaining all GPU instance types could lead to prohibitively high costs. To avoid the financial strain of idle instances, we implemented a “standby instances” mechanism. Rather than preparing for the maximum potential load, we maintained a calculated number of standby instances that match the... See more

core components of Deep RL that enabled success like AlphaGo: self-play and look-ahead planning.

Self-play is the idea that an agent can improve its gameplay by playing against slightly different versions of itself because it’ll progressively encounter more challenging situations. In the space of LLMs, it is almost certain that the largest portion... See more

A solution is to self-host an open-sourced or custom fine-tuned LLM. Opting for a self-hosted model can reduce costs dramatically - but with additional development time, maintenance overhead, and possible performance implications. Considering self-hosted solutions requires weighing these different trade-offs carefully.

In general, I see LLMs to be used in two broad categories: data processing, which is more of a worker use-cases, where the latency isn't the biggest issue but rather quality, and in user-interactions, where latency is a big factor. I think for the faster case a faster fallback is necessary. Or you escalate upwards, you first rely on a smaller more... See more

𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦: it will improve your LLM performance on given use cases (e.g., coding, extracting text, etc.). Mainly, the LLM will specialize in a given task (a specialist will always beat a generalist in its domain)

𝘤𝘰𝘯𝘵𝘳𝘰𝘭: you can refine how your model should behave on specific inputs and outputs, resulting in a more robust product

𝘮𝘰𝘥𝘶𝘭𝘢𝘳𝘪𝘻𝘢𝘵𝘪𝘰𝘯:... See more

GPT-4 Turbo performs better than our previous models on tasks that require the careful following of instructions, such as generating specific formats (e.g., “always respond in XML”). It also supports our new JSON mode, which ensures the model will respond with valid JSON. The new API parameter response_format enables the model to constrain its... See more