kennethreitz/instructor
1
0
Fork 0
mirror of https://github.com/kennethreitz/instructor.git synced 2026-06-05 22:50:18 +00:00
Code Issues Packages Projects Releases Wiki Activity

sellout.md

Browse Source
This commit is contained in:
Jason Liu
2023-10-25 22:14:40 -04:00
parent 4b7eb2a49c
commit 0a5aad84a9
4 changed files with 10 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/blog/posts/distilation-part1.md
+3
View File
@@ -126,3 +126,6 @@ With this, you can swap the function implementation, making it backward compatib
## Conclusion
We've seen how `Instructor` can make your life easier, from fine-tuning to distillation. Now if you're thinking wow, I'd love a backend service to do this for continously, you're in luck! Please check out the survey at [useinstructor.com](https://useinstructor.com) and let us know who you are.
If you enjoy the content or want to try out `instructor` please check out the [github](https://github.com/jxnl/instructor) and give us a star!
docs/blog/posts/introduction.md
+3 -1
View File
@@ -175,4 +175,6 @@ class MaybeUser(BaseModel):
## Conclusion
Instructor, with Pydantic, simplifies interaction with language models. It is usable for both experienced and new developers.
Instructor, with Pydantic, simplifies interaction with language models. It is usable for both experienced and new developers.
If you enjoy the content or want to try out `instructor` please check out the [github](https://github.com/jxnl/instructor) and give us a star!
docs/blog/posts/rag-and-beyond.md
+2 -1
View File
@@ -231,4 +231,5 @@ Here I want to show that `instructor`` isn’t just about data extraction. It’
I believe collaboration between domain experts and AI engineers is the key to enable advanced tool use. I’ve been building a new tool on top of instructor that enables seamless collaboration and experimentation on LLMs with structured outputs. If you’re interested, visit [useinstructor.com](https://useinstructor.com) and take our survey to join the waitlist.
Together, let’s create tools that are as brilliant as the minds that use them.
If you enjoy the content or want to try out `instructor` please check out the [github](https://github.com/jxnl/instructor) and give us a star!
docs/blog/posts/validation-part1.md
+2
View File
@@ -485,3 +485,5 @@ In this example, even though there is no code explicitly transforming the name t
We've examined the limitations of traditional validation and how modern tools and AI can offer more robust solutions. From the simplicity of Pydantic and Instructor to the dynamic validation capabilities of LLMs, the landscape of validation is changing but without needing to introduce new contepts. With advanced techniques like validating attributes, chain of thought, and contextual validation, it's clear that the future of validation is not just about preventing bad data but about allowing llms to understand the data and correcting it.
Remember, validation and error handling are crucial for ensuring the quality and reliability of AI systems. By applying the concepts discussed in this post, you can enhance the control flow and improve the overall performance of your AI application without introducting new concepts and standards.
If you enjoy the content or want to try out `instructor` please check out the [github](https://github.com/jxnl/instructor) and give us a star!