@@ -24,10 +24,10 @@ image: ./preview.png
2424We are excited to announce that CodeRabbit has acquired
2525[ FluxNinja] ( https://fluxninja.com ) , a startup that provides a platform for
2626building scalable generative AI applications. This acquisition will allow us to
27- ship new use cases at an industrial-scale while sustaining our rapidly growing
28- user base. FluxNinja's Aperture product provides advanced rate-limiting,
29- caching, and request prioritization capabilitiesfor building reliable and
30- cost-effective AI workflows.
27+ ship new use cases at an industrial-pace while sustaining our rapidly growing
28+ user base. FluxNinja's Aperture product provides advanced rate & concurrency
29+ limiting, caching, and request prioritization capabilitiesthat are essential
30+ for reliable and cost-effective AI workflows.
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@@ -73,16 +73,17 @@ platform that can solve the following problems:
7373 tricked into divulging sensitive information, which could include our base
7474 prompts.
7575
76- - Validating qualityof inference : Generative AI models consume text and output
76+ - Validation & qualitychecks : Generative AI models consume text and output
7777 text. On the other hand, traditional code and APIs required structured data.
7878 Therefore, the prompt service needs to expose a RESTful or gRPC API that can
7979 be consumed by the other services in the workflow. We touched upon the
8080 rendering of prompts based on structured requests in the previous point, but
81- the prompt service also needs to parse and validate responses into structured
82- data. This is a non-trivial problem, and multiple tries are often required to
83- ensure that the response is thorough. For instance, we found that when we pack
84- multiple files in a single code review prompt, AI models often miss hunks
85- within a file or miss files altogether, leading to incomplete reviews.
81+ the prompt service also needs to parse, validate responses into structured
82+ data and measure the quality of the inference. This is a non-trivial problem,
83+ and multiple tries are often required to ensure that the response is thorough
84+ and meets the quality bar. For instance, we found that when we pack multiple
85+ files in a single code review prompt, AI models often miss hunks within a file
86+ or miss files altogether, leading to incomplete reviews.
8687
8788- Observability: One key challenge with generative AI and prompting is that it's
8889 inherently non-deterministic. The same prompt can result in vastly different