4 Levels of LLM Customization With Dataiku
Summary
TLDRThis 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
🤖 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.
🔍 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.
Mindmap
Keywords
💡Large Language Models (LLMs)
💡Customization
💡Dataiku
💡Prompt Studios
💡Zero-shot Learning
💡In-context Learning
💡Retrieval-Augmented Generation (RAG)
💡Vector Store
💡Lang Chain
💡REACT Method
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
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