@akshay_pachaar: Building a production-grade RA...
@akshay_pachaar
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Jan 12, 2024
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Building a production-grade RAG Applications!
In the enterprise world, LLMs are primarily used for Retrieval-Augmented Generation on custom knowledge bases.
These applications may seem deceptively simple and easy to prototype, but pushing them to production can be a painful process.
Here's are some of the key AI Engineering challenges:
1️⃣ Parsing complex docs and PDFs:
Most open-source libraries don't do a great job here, the complexities can include tables spanning multiple pages, nested sections within paragraphs, inconsistent fonts throughout the document etc.
2️⃣ Chunking
Chunking is one of the most challenging & essential component in LLM applications for two primary reasons:
- Preserving semantic relevance
- Fitting content within context length of LLMs
Things to Keep in Mind When Chunking:
- Nature of the content
- Embedding model: which chunk sizes does it perform optimally on?
- Length and complexity of user queries
- Use of retrieved results: The purpose must be clear, e.g., question answering, summarization, etc.
3️⃣ Data pipelines
The LLM app should have access to any updates in the data
4️⃣ Custom front-ends
Ideally, you need a custom front-end on top of your LLM app and/or access the LLM app from your Slack or Team channel
5️⃣ Complex orchestration
To effectively handle complex prompts, you need to be able to coordinate between different document retrievers and/or vector stores.
6️⃣ SQL and Code Execution:
Depending on the complexity of your LLM application, you may need to execute code or SQL queries.
7️⃣ LLM choice:
Depending on your use case, you may want to use a cheaper open-source LLM or a closed-source API.
8️⃣ Evaluation & Ease of iteration:
Just like with any other ML app, you need a way to measure accuracy and iterate on the app. If you don't repeat and evaluate the app, the chances of it not being used are very high.
LLM apps, just like any software, need monitoring, testing, and maintenance.
@AbacusAI has successfully deployed dozens of LLMs and effectively handles all these challenges.
If you're looking to save time and prioritize what truly matters for your organization, checking them out is a smart move.
Thanks for reading!
Find me → @akshay_pachaar ✔️
For more content like this!
Cheers!🥂
In the enterprise world, LLMs are primarily used for Retrieval-Augmented Generation on custom knowledge bases.
These applications may seem deceptively simple and easy to prototype, but pushing them to production can be a painful process.
Here's are some of the key AI Engineering challenges:
1️⃣ Parsing complex docs and PDFs:
Most open-source libraries don't do a great job here, the complexities can include tables spanning multiple pages, nested sections within paragraphs, inconsistent fonts throughout the document etc.
2️⃣ Chunking
Chunking is one of the most challenging & essential component in LLM applications for two primary reasons:
- Preserving semantic relevance
- Fitting content within context length of LLMs
Things to Keep in Mind When Chunking:
- Nature of the content
- Embedding model: which chunk sizes does it perform optimally on?
- Length and complexity of user queries
- Use of retrieved results: The purpose must be clear, e.g., question answering, summarization, etc.
3️⃣ Data pipelines
The LLM app should have access to any updates in the data
4️⃣ Custom front-ends
Ideally, you need a custom front-end on top of your LLM app and/or access the LLM app from your Slack or Team channel
5️⃣ Complex orchestration
To effectively handle complex prompts, you need to be able to coordinate between different document retrievers and/or vector stores.
6️⃣ SQL and Code Execution:
Depending on the complexity of your LLM application, you may need to execute code or SQL queries.
7️⃣ LLM choice:
Depending on your use case, you may want to use a cheaper open-source LLM or a closed-source API.
8️⃣ Evaluation & Ease of iteration:
Just like with any other ML app, you need a way to measure accuracy and iterate on the app. If you don't repeat and evaluate the app, the chances of it not being used are very high.
LLM apps, just like any software, need monitoring, testing, and maintenance.
@AbacusAI has successfully deployed dozens of LLMs and effectively handles all these challenges.
If you're looking to save time and prioritize what truly matters for your organization, checking them out is a smart move.
Thanks for reading!
Find me → @akshay_pachaar ✔️
For more content like this!
Cheers!🥂
