4 Levels of LLM Customization With Dataiku

Summary

This video explores how to adapt large language models (LLMs) for business use, offering a four-tier framework for customization. It begins with using LLMs out of the box for text applications, then moves to crafting tailored prompts for specialized tasks. The third level introduces retrieval augmented generation for question-answering applications, while the fourth covers advanced techniques like fine-tuning and reinforcement learning. Dataiku's platform simplifies these processes, providing tools for prompt design, cost estimation, and semantic search, enabling businesses to harness the full potential of LLMs without extensive coding knowledge.

Takeaways

• 🧠 Large Language Models (LLMs) have the potential to revolutionize business operations by adapting to various domains and use cases.
• 📦 To harness LLMs effectively, businesses need to adapt them to their specific needs and combine them with other tools and models.
• 🔍 Dataiku offers a framework with four levels of complexity for customizing LLM behavior, making AI techniques accessible to a broader audience.
• 🚀 Level 1: Utilize LLMs out of the box for text applications, with Dataiku providing visual components for easy integration and AI-generated metadata.
• 📝 Level 2: Craft tailored prompts using Dataiku's Prompt Studio to provide additional context or instructions for specialized tasks.
• 🔢 Level 3: Implement Retrieval-Augmented Generation (RAG) for question and answer applications, where specialized knowledge is required.
• 📚 RAG involves encoding textual information into embeddings for efficient semantic search over document collections to provide accurate responses.
• 🛠️ Dataiku simplifies RAG by providing visual components for creating vector stores from documents and orchestrating queries with enriched context.
• 📈 Level 4: Explore advanced customization techniques like supervised fine-tuning, pre-training, or reinforcement learning for highly specialized tasks.
• 💡 Dataiku's framework supports the exploration of sophisticated LLM customization but emphasizes that advanced techniques are rarely needed.
• 🔑 The script highlights the importance of automating tedious tasks with LLMs to free up time for higher value activities for skilled workers.

Q & A

What is the main topic of the video script?

The main topic of the video script is the customization of large language models (LLMs) for business applications, including adapting generic LLMs to specific domains and integrating them with other models and tools.

How does Dataiku simplify the integration of LLMs into existing pipelines?

Dataiku simplifies the integration of LLMs into existing pipelines with intuitive visual components, allowing for the infusion of AI-generated metadata without the need for users to know how to code.

What are some of the text applications that can be simplified and supercharged using LLMs?

Text applications such as automatic document classification, summarization, and instant answering of multiple questions about data across various languages can be simplified and supercharged using LLMs.

What is Dataiku's approach to providing transparency, scalability, and cost control over LLM queries?

Dataiku provides teams with unprecedented transparency, scalability, and cost control over their LLM queries by offering an easy-to-use interface and visual components that integrate with both private models and third-party services.

What is a tailored prompt and how does Dataiku's Prompt Studio help in crafting them?

A tailored prompt is a customized input designed to guide an LLM towards producing specific outputs relevant to a business task. Dataiku's Prompt Studio helps in crafting these prompts by providing an interface to design, compare, and evaluate prompts across different models and providers.

What are the two types of learning mentioned in the script for understanding the intended task by an LLM?

The two types of learning mentioned are zero-shot learning, where no examples or labeled data are provided, and in-context learning, where examples of inputs and expected outputs are added to help the LLM understand the intended task.

How does Dataiku's Prompt Studio assist in validating compliance and estimating costs?

Dataiku's Prompt Studio assists in validating compliance by allowing users to test prompts against real data and ensuring they meet common standards. It also provides cost estimates, enabling users to make trade-off decisions between cost and performance during the design phase.

What are some efficiency use cases for automating tasks with LLMs?

Efficiency use cases for automating tasks with LLMs include automating tedious, time-consuming tasks that are currently performed manually by knowledge workers, freeing up their time for higher-value activities.

What is Retrieval-Augmented Generation (RAG) and how does it help in question and answer applications?

Retrieval-Augmented Generation (RAG) is a technique that encodes textual information into numeric format and stores it as embeddings in a vector store. This enables efficient semantic search over a document collection to quickly and accurately locate and cite the right information for question and answer applications.

How does Dataiku facilitate the implementation of Retrieval-Augmented Generation?

Dataiku facilitates the implementation of RAG by providing visual components that extract raw text from files, create a vector store based on documents, and orchestrate the query to the LLM with enriched context, including handling the web application interaction.

What advanced techniques are mentioned for further customization of LLMs?

Advanced techniques mentioned for further customization of LLMs include supervised fine-tuning, pre-training, reinforcement learning, and the use of external tools such as Lang chain or the React method for complex reasoning and action-based tasks.

What is the purpose of the Lang chain toolkit in the context of LLM customization?

The Lang chain toolkit is used for orchestrating the underlying logic of retrieve-then-read pipelines, providing a powerful Python and JavaScript toolkit for more sophisticated forms of LLM customization.

Outlines

00:00

🤖 Customizing LLMs for Business Efficiency

This paragraph introduces the potential of Large Language Models (LLMs) to transform business operations. It emphasizes the necessity of adapting generic LLMs to specific domains and use cases, and combining them with other models and tools. The video promises to outline a framework with four levels of complexity for customizing LLM behavior, starting with using LLMs out of the box for text applications. Dataiku is highlighted for simplifying integration with visual components and providing AI-generated metadata for tasks like document classification and summarization, all without coding knowledge. The paragraph also touches on the benefits of prompt crafting with Dataiku's Prompt Studio for more specialized tasks, including zero-shot and few-shot learning, and the importance of cost estimates in the design phase.

05:01

🔍 Advanced LLM Customization with Retrieval-Augmented Generation

The second paragraph delves into more advanced methods for customizing LLMs, focusing on Retrieval-Augmented Generation (RAG) for question-and-answer applications. It explains how RAG encodes text into embeddings for efficient semantic search over document collections, allowing for accurate retrieval of information. Dataiku facilitates this process by offering visual components to extract text and create vector stores, enabling the orchestration of queries to LLMs with enriched context. The paragraph also briefly mentions the highest level of LLM customization, which includes supervised fine-tuning, pre-training, and reinforcement learning, requiring substantial training data and computational resources. It concludes by suggesting that while Dataiku supports these sophisticated techniques, they are only necessary in a minority of cases, and that the methods from the previous levels are usually sufficient for most business needs.

Keywords

💡Large Language Models (LLMs)

Large Language Models, or LLMs, refer to artificial intelligence systems that are designed to process and generate human-like language. They are capable of understanding context, syntax, and semantics, which allows them to perform a wide range of language-related tasks. In the video's context, LLMs are highlighted for their potential to revolutionize business operations by simplifying and enhancing text applications. The script discusses how to adapt these models to specific domains and use cases, emphasizing their ability to automate tasks and provide insights.

💡Customization

Customization in the video refers to the process of adapting generic LLMs to fit specific business needs or use cases. It is a key concept as it outlines the framework for tailoring LLMs' behavior to achieve desired outcomes. The script mentions four levels of customization, from using LLMs out of the box to more advanced techniques like supervised fine-tuning, each with its own set of technical methods and applications.

💡Dataiku

Dataiku is a company mentioned in the script that provides a platform for data science and machine learning. It simplifies the integration of LLMs into existing business pipelines and offers tools like visual components and prompt studios to make AI-generated metadata and prompt design more accessible. The script highlights Dataiku's role in providing transparency, scalability, and cost control over LLM queries.

💡Prompt Studios

Prompt Studios, as described in the script, is a feature within Dataiku that allows users to design, compare, and evaluate prompts across different models and providers. It is used to craft tailored prompts that can provide additional context or instructions to LLMs, helping to bridge the gap between the models' natural outputs and specific business tasks. The script illustrates how Prompt Studios can aid in achieving business goals through better standardization and reusability of prompts.

💡Zero-shot Learning

Zero-shot learning is a concept within machine learning where a model is able to perform a task without any examples or labeled data for that specific task. In the context of the video, it is one of the strategies for elevating an LLM's acumen by providing it with the ability to understand and perform a task based on its pre-existing knowledge, without additional training data for that task.

💡In-context Learning

In-context learning, also known as few-shot modeling, is another strategy mentioned in the script where the LLM is provided with examples of inputs and the expected outputs to perform a specific task. This method helps the model to understand the intended task better by giving it a few examples, which is particularly useful when the task is specialized or requires a nuanced understanding.

💡Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation, or RAG, is a technique discussed in the script for question and answer type applications. It involves encoding textual information into a numeric format, storing it as embeddings, and enabling efficient semantic search over a document collection. RAG allows for the retrieval of accurate and up-to-date responses from a proprietary knowledge base, which is crucial for applications requiring high precision and compliance.

💡Vector Store

A vector store, as mentioned in the script, is a database or file index that stores the vectorized form of textual information, known as embeddings. This store allows for semantic search and retrieval of relevant information from a knowledge base, which is essential for RAG and other advanced LLM applications that require accessing large volumes of specialized knowledge.

💡Lang Chain

Lang Chain is a toolkit mentioned in the script for orchestrating the underlying logic of retrieve-then-read pipelines. It is used in conjunction with advanced LLM customization techniques to manage complex reasoning and action-based tasks. Lang Chain provides a framework for integrating external tools and managing the flow of information in sophisticated LLM applications.

💡REACT Method

The REACT method, as briefly touched upon in the script, is a sophisticated approach for complex reasoning and action-based tasks. It is part of the advanced techniques for LLM customization that may be necessary in nuanced language scenarios where standard methods are insufficient. The script suggests that while Dataiku's framework can support exploration of such advanced customization, these techniques are typically needed in only a minority of cases.

Highlights

Large language models (LLMs) have the potential to revolutionize business operations.

To capitalize on LLMs, they need to be adapted to specific domains and use cases.

Dataiku offers a framework with four levels of complexity for customizing LLM behavior.

Level one involves using LLMs out of the box for text applications.

Dataiku simplifies integration with visual components and AI-generated metadata.

LLMs can be used for automatic document classification, summarization, and multi-language question answering.

Dataiku provides transparency, scalability, and cost control over LLM queries without coding knowledge.

Level two involves crafting tailored prompts for specialized tasks.

Dataiku's Prompt Studio allows for the design and evaluation of prompts across models.

Prompt Studio supports zero-shot learning and in-context learning with examples.

Cost estimates are provided for trade-off decisions between cost and performance.

Automating tedious tasks with LLMs can free up time for higher value activities.

Level three introduces Retrieval-Augmented Generation (RAG) for specialized knowledge retrieval.

RAG encodes information into embeddings for efficient semantic search.

Dataiku provides components for creating vector stores and semantic search integration.

Level four covers advanced customization techniques like fine-tuning and reinforcement learning.

Advanced techniques require high-quality training data and significant compute resources.

Dataiku's framework supports exploration of sophisticated LLM customization.

External tools like Lang chain and React method can be incorporated for complex tasks.

Dataiku's website offers more information on building effective LLM applications.

Transcripts

[Music]

the versatility of large language models

or llms for short gives them the

potential to revolutionize the way you

do business but to fully capitalize on

their value you need to know how to

adapt generic llms to your domain and

use case as well as effectively combine

them with other purpose-built models and

tools

in this video we'll cover some important

considerations and provide a framework

with four levels of increasing

complexity for customizing an llm's

Behavior

for each level we'll delve into the

technical methods you'll likely apply

and how dataiku makes these techniques

accessible to more people

luckily the first level may be easier

than you think

simply harness the power of llms right

out of the box to simplify and

supercharge your text applications

dataiku simplifies this process with

intuitive visual components to integrate

llms whether private models or

third-party services and Infuse AI

generated metadata into your existing

Pipelines

from automatic document classification

or summarization

to instantly answering multiple

questions about your data across a

smorgasbord of languages

dataiku provides teams with

unprecedented transparency scalability

and cost control over their llm queries

all without users needing to know how to

code

now what if your task is more

specialized and you need to provide

additional context or instructions to

bridge the gap between an llm's natural

outputs and your specific task

to elevate your llm's Acumen the next

best strategy is to craft more tailored

prompts

with data ACU's prompt Studios you can

design compare and evaluate prompts

across models and providers to identify

and operationalize the best context for

achieving your business goals

prompt Studios provide an easy to use

interface with sections where you can

create prompt templates for better

standardization and reusability

choose models you want to explore and

compare results between

and explain your task in plain language

any language

you can do zero shot learning where you

provide no examples or label data to

help the llm understand your intended

task or you can perform in-context

learning by adding examples of inputs

and the expected outputs this is also

known as few shot modeling

test your prompt against Real data to

see how it performs

and validate compliance against common

standards no more worrying about valid

Json

datacus prompt Studios also provide cost

estimates empowering you to make

trade-off decisions between cost and

performance and gauge the financial

impact of embedding generative AI into

your pipelines during the design phase

rather than after the fact

ready for some good news

using just the llm approaches discussed

in these first two levels a huge number

of tedious time-consuming tasks

performed manually by your knowledge

workers today can be automated

these types of efficiency use cases are

essentially low hanging fruit for you to

tackle first while learning the llm

ropes and solving them will free up

precious time for your skilled workers

that they can then redirect towards

higher value activities

so then what are some situations where

we might need to level up to more

advanced methods and what are those

methods

as an example let's consider question

and answer type applications

say you want to retrieve answers from

your own proprietary knowledge Bank to

ensure accurate up-to-date responses and

mitigate the risk of hallucinations

in some cases the volume of specialized

background knowledge you need to provide

is context is too large to fit in the

model's allowable context window

or perhaps you want to add a layer of

logic that produces on-the-fly data

visualizations to help users dynamically

explore the answers and derive insights

these are use cases where the third

level of complexity comes into play

to give more practical examples it's

likely that knowledge workers like your

customer service reps technical support

agents or legal analysts often need to

look up facts from policy manuals case

law and other such reference material to

answer questions

in some cases the correct and most

up-to-date answers may be sourced from

internal documents that a generic model

was never trained on

further you may require a citation of

where the answer came from for

compliance purposes

the core method that enables the type of

application we see here is called

retrieval augmented generation or rag

for short

this technique encodes textual

information into numeric format and

stores it as embeddings in a vector

store either in a file index or database

in turn this vectorized knowledge base

enables efficient semantic search over

your document collection so you can

quickly and accurately locate and cite

the right information for question and

answer type applications

in dataiku we make retrieval augmented

generation Easy by providing visual

components that extract raw text from

files and create a vector store based on

your documents

the Q to semantic search to retrieve the

most relevant pieces of knowledge

orchestrate the query to the llm with

the enriched context and even handle the

web application your knowledge workers

will interact with so you don't need to

develop a custom front end or chat bot

finally let's touch briefly on the most

sophisticated and challenging level of

llm customization

this includes Advanced Techniques such

as supervised fine-tuning pre-training

or reinforcement learning to adjust a

pre-trained model so that it can better

accomplish certain tasks be more suited

for a given domain or align with

instructions more closely

these approaches typically require

copious amounts of high quality training

data and a significant investment in

compute infrastructure

we won't go into it in detail here but

for even more customization you can

incorporate external tools with an

approach referred to as llm agents

orchestrate the underlying logic of

these retrieve then read pipelines with

Lang chain a powerful Python and

JavaScript toolkit

or use the react method for complex

reasoning and action-based tasks

although dataiku's framework is equipped

to help you explore these highly

sophisticated forms of llm customization

remember that these Advanced Techniques

are only needed in a minority of cases

with nuanced language and that the

techniques presented in the previous

three levels are generally sufficient

for molding the behavior of an llm

to learn even more about how dataiku can

help you build effective llm

applications to take your business to

the next level visit our website and

thanks for watching