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by zensorsolutions
81 days ago
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This is interesting — especially the focus on reducing incorrect outputs rather than just improving generation. Feels like a common pattern across AI tools right now:
getting something generated is easy, getting something reliable enough to act on is the hard part. Have you noticed whether developers trust the output more when the source of context is explicit (like SDKs), vs inferred? |
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The first is simply what the model appears to be grounding on. If it’s pulling up SDK method names and library docs, developers feel much better about the result than when it looks like the model is piecing things together from web search, blogs, GitHub snippets, or other unofficial sources.
We’ve also found that even among authoritative sources, there’s a real difference between an OpenAPI spec or API reference, and SDK documentation. An OpenAPI spec or API reference is authoritative, but it still leaves the model with a lot to figure out. It has to infer how to authenticate, how to handle pagination, what to do in case of errors/failure, and then turn all of that into working code in the right place in an existing app.
That’s a long chain of inference, and every extra step is another place where things can go wrong.
SDK context cuts out a lot of that. Much of the complexity is already wrapped in the library, so the model is usually figuring out which SDK method to call and how to wire it up, instead of inventing the integration from scratch.
In practice, the biggest trust builder is the outcome: does the generated code run on the first try?
That’s the outcome developers care about most, and it’s the pattern we saw in our user tests. Context Plugins help get much closer to that outcome.
We’ve published a case study that goes deeper into the numbers here: https://www.apimatic.io/product/context-plugins/case-study