We may earn an affiliate commission when you visit our partners.
Anton Troynikov
Information Retrieval (IR) and Retrieval Augmented Generation (RAG) are only effective if the information retrieved from a database as a result of a query is relevant to the query and its application.
Read more
Information Retrieval (IR) and Retrieval Augmented Generation (RAG) are only effective if the information retrieved from a database as a result of a query is relevant to the query and its application.
Read more
Information Retrieval (IR) and Retrieval Augmented Generation (RAG) are only effective if the information retrieved from a database as a result of a query is relevant to the query and its application.
Too often, queries return semantically similar results but don’t answer the question posed. They may also return irrelevant material which can distract the LLM from the correct results.
This course teaches advanced retrieval techniques to improve the relevancy of retrieved results.
The techniques covered include:
1. Query Expansion: Expanding user queries improves information retrieval by including related concepts and keywords. Utilizing an LLM makes this traditional technique even more effective. Another form of expansion has the LLM suggest a possible answer to the query which is then included in the query.
2. Cross-encoder reranking: Reranking retrieval results to select the results most relevant to your query improves your results.
3. Training and utilizing Embedding Adapters: Adding an adapter layer to reshape embeddings can improve retrieval by emphasizing elements relevant to your application.
Register for this course and see more details by visiting:
OpenCourser.com/course/ogglnb/advanced
Two deals to help you save
We found two deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
What's inside
Syllabus
Project Overview
Information Retrieval (IR) and Retrieval Augmented Generation (RAG) are only effective if the information retrieved from a database as a result of a query is relevant to the query and its application.
Too often, queries return semantically similar results but don’t answer the question posed. They may also return irrelevant material which can distract the LLM from the correct results.
This course teaches advanced retrieval techniques to improve the relevancy of retrieved results.
The techniques covered include:
(1) Query Expansion: Expanding user queries improves information retrieval by including related concepts and keywords. Utilizing an LLM makes this traditional technique even more effective. Another form of expansion has the LLM suggest a possible answer to the query which is then included in the query.
(2) Cross-encoder reranking: Reranking retrieval results to select the results most relevant to your query improves your results.
(3) Training and utilizing Embedding Adapters: Adding an adapter layer to reshape embeddings can improve retrieval by emphasizing elements relevant to your application.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches advanced retrieval techniques to improve relevancy of search results, crucial for effective information retrieval and retrieval augmented generation
Instructors Anton Troynikov are seasoned experts in the field
Suitable for learners seeking to enhance their information retrieval skills
Save this course
Save Advanced Retrieval for AI with Chroma to your list so you can find it easily later:
Save
Save
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Advanced Retrieval for AI with Chroma with these
activities:
Review basic probability and statistics
Show steps
Refresh your probability and statistics skills to prepare you for the course's more advanced concepts.
Browse courses on
Probability
Show steps
-
Review probability distributions, such as the normal distribution, binomial distribution, and Poisson distribution.
-
Practice solving probability problems involving conditional probability, Bayes' theorem, and expected value.
-
Review basic statistical concepts, such as mean, median, mode, variance, and standard deviation.
-
Practice performing hypothesis testing and calculating confidence intervals.
Follow tutorials on query expansion techniques
Show steps
Follow guided tutorials on query expansion techniques to reinforce your understanding and gain hands-on practice.
Show steps
-
Search for online tutorials on query expansion
-
Select a tutorial that aligns with your learning style
-
Complete the tutorial and apply the techniques to a sample dataset
Study NLP Foundations
Show steps
Improve your understanding of NLP concepts.
Browse courses on
Natural Language Processing
Show steps
-
Review basic NLP concepts, such as tokenization, stemming, and lemmatization.
-
Explore different NLP techniques, such as text classification, sentiment analysis, and named entity recognition.
-
Practice applying NLP techniques in practical scenarios.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Review query expansion techniques
Show steps
Refresh your skills on query expansion techniques, which are key for improving the relevancy of retrieved results in information retrieval systems.
Show steps
-
Review online tutorials on query expansion
-
Practice query expansion techniques on a sample dataset
-
Share your findings with a peer or mentor
Review cross-encoder reranking techniques
Show steps
Refresh your knowledge on cross-encoder reranking techniques, which are important for re-ranking retrieval results to select the most relevant results for your query.
Show steps
-
Read research papers on cross-encoder reranking
-
Implement a cross-encoder reranking model on a sample dataset
-
Evaluate the performance of your model
Practice query expansion and cross-encoder reranking on sample datasets
Show steps
Solidify your understanding of query expansion and cross-encoder reranking by practicing on sample datasets.
Show steps
-
Find sample datasets related to your application
-
Apply query expansion and cross-encoder reranking techniques to the datasets
-
Analyze the results and identify areas for improvement
Complete coding challenges related to IR and RAG
Show steps
Practice solving coding challenges to improve your understanding of IR and RAG techniques.
Show steps
-
Find coding challenges related to IR and RAG on platforms like LeetCode or HackerRank.
-
Attempt to solve the challenges, focusing on implementing different IR and RAG algorithms.
-
Review your solutions and identify areas for improvement.
Attend an NLP Workshop
Show steps
Learn from experts and network with other NLP professionals.
Browse courses on
NLP
Show steps
-
Find and register for an NLP workshop that covers topics relevant to IR and RAG.
-
Attend the workshop and actively participate in discussions and hands-on exercises.
Discuss advanced retrieval techniques with peers
Show steps
Engage with peers to exchange knowledge, clarify concepts, and share insights on advanced retrieval techniques.
Show steps
-
Join or start a study group focused on advanced retrieval techniques
-
Prepare discussion topics and questions related to query expansion, cross-encoder reranking, and embedding adapters
-
Actively participate in discussions, ask questions, and share your own knowledge
Solve Coding Challenges
Show steps
Develop your programming skills and apply them to NLP tasks.
Browse courses on
Information Retrieval
Show steps
-
Solve coding challenges related to IR and RAG.
-
Implement and evaluate different query expansion and cross-encoder reranking algorithms.
Create a blog post or article on a specific IR or RAG technique
Show steps
Demonstrate your understanding of IR and RAG by creating a blog post or article that explains a specific technique.
Show steps
-
Choose a specific IR or RAG technique to focus on.
-
Research the technique thoroughly and gather relevant information.
-
Write a blog post or article that clearly explains the technique, its applications, and potential benefits.
-
Share your blog post or article online and promote it to relevant audiences.
Build a Retrieval-Based Chatbot
Show steps
Apply your knowledge to build a practical NLP application.
Browse courses on
Information Retrieval
Show steps
-
Design and implement a retrieval-based chatbot using techniques covered in the course.
-
Evaluate the performance of your chatbot and make improvements.
-
Share your chatbot with others and get feedback.
Contribute to an open-source project related to advanced retrieval techniques
Show steps
Apply your knowledge and skills by contributing to an open-source project in the field of advanced retrieval techniques.
Show steps
-
Identify open-source projects that align with your interests and skillset
-
Contact the project maintainers to express your interest in contributing
-
Review the project documentation and codebase
-
Make a meaningful contribution to the project
Implement a retrieval system that utilizes advanced retrieval techniques
Show steps
Demonstrate your mastery of advanced retrieval techniques by implementing a complete retrieval system that incorporates query expansion, cross-encoder reranking, and embedding adapters.
Show steps
-
Gather a dataset for your retrieval system
-
Design and implement the architecture of your retrieval system
-
Train and evaluate your retrieval system
-
Deploy your retrieval system and evaluate its performance
Review basic probability and statistics
Show steps
Refresh your probability and statistics skills to prepare you for the course's more advanced concepts.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review probability distributions, such as the normal distribution, binomial distribution, and Poisson distribution.
- Practice solving probability problems involving conditional probability, Bayes' theorem, and expected value.
- Review basic statistical concepts, such as mean, median, mode, variance, and standard deviation.
- Practice performing hypothesis testing and calculating confidence intervals.
Follow tutorials on query expansion techniques
Show steps
Follow guided tutorials on query expansion techniques to reinforce your understanding and gain hands-on practice.
Show steps
Show steps
Show steps
- Search for online tutorials on query expansion
- Select a tutorial that aligns with your learning style
- Complete the tutorial and apply the techniques to a sample dataset
Study NLP Foundations
Show steps
Improve your understanding of NLP concepts.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Review basic NLP concepts, such as tokenization, stemming, and lemmatization.
- Explore different NLP techniques, such as text classification, sentiment analysis, and named entity recognition.
- Practice applying NLP techniques in practical scenarios.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Review query expansion techniques
Show steps
Refresh your skills on query expansion techniques, which are key for improving the relevancy of retrieved results in information retrieval systems.
Show steps
Show steps
Show steps
- Review online tutorials on query expansion
- Practice query expansion techniques on a sample dataset
- Share your findings with a peer or mentor
Review cross-encoder reranking techniques
Show steps
Refresh your knowledge on cross-encoder reranking techniques, which are important for re-ranking retrieval results to select the most relevant results for your query.
Show steps
Show steps
Show steps
- Read research papers on cross-encoder reranking
- Implement a cross-encoder reranking model on a sample dataset
- Evaluate the performance of your model
Practice query expansion and cross-encoder reranking on sample datasets
Show steps
Solidify your understanding of query expansion and cross-encoder reranking by practicing on sample datasets.
Show steps
Show steps
Show steps
- Find sample datasets related to your application
- Apply query expansion and cross-encoder reranking techniques to the datasets
- Analyze the results and identify areas for improvement
Complete coding challenges related to IR and RAG
Show steps
Practice solving coding challenges to improve your understanding of IR and RAG techniques.
Show steps
Show steps
Show steps
- Find coding challenges related to IR and RAG on platforms like LeetCode or HackerRank.
- Attempt to solve the challenges, focusing on implementing different IR and RAG algorithms.
- Review your solutions and identify areas for improvement.
Attend an NLP Workshop
Show steps
Learn from experts and network with other NLP professionals.
Browse courses on
NLP
Show steps
Show steps
Show steps
- Find and register for an NLP workshop that covers topics relevant to IR and RAG.
- Attend the workshop and actively participate in discussions and hands-on exercises.
Discuss advanced retrieval techniques with peers
Show steps
Engage with peers to exchange knowledge, clarify concepts, and share insights on advanced retrieval techniques.
Show steps
Show steps
Show steps
- Join or start a study group focused on advanced retrieval techniques
- Prepare discussion topics and questions related to query expansion, cross-encoder reranking, and embedding adapters
- Actively participate in discussions, ask questions, and share your own knowledge
Solve Coding Challenges
Show steps
Develop your programming skills and apply them to NLP tasks.
Browse courses on
Information Retrieval
Show steps
Show steps
Show steps
- Solve coding challenges related to IR and RAG.
- Implement and evaluate different query expansion and cross-encoder reranking algorithms.
Create a blog post or article on a specific IR or RAG technique
Show steps
Demonstrate your understanding of IR and RAG by creating a blog post or article that explains a specific technique.
Show steps
Show steps
Show steps
- Choose a specific IR or RAG technique to focus on.
- Research the technique thoroughly and gather relevant information.
- Write a blog post or article that clearly explains the technique, its applications, and potential benefits.
- Share your blog post or article online and promote it to relevant audiences.
Build a Retrieval-Based Chatbot
Show steps
Apply your knowledge to build a practical NLP application.
Browse courses on
Information Retrieval
Show steps
Show steps
Show steps
- Design and implement a retrieval-based chatbot using techniques covered in the course.
- Evaluate the performance of your chatbot and make improvements.
- Share your chatbot with others and get feedback.
Contribute to an open-source project related to advanced retrieval techniques
Show steps
Apply your knowledge and skills by contributing to an open-source project in the field of advanced retrieval techniques.
Show steps
Show steps
Show steps
- Identify open-source projects that align with your interests and skillset
- Contact the project maintainers to express your interest in contributing
- Review the project documentation and codebase
- Make a meaningful contribution to the project
Implement a retrieval system that utilizes advanced retrieval techniques
Show steps
Demonstrate your mastery of advanced retrieval techniques by implementing a complete retrieval system that incorporates query expansion, cross-encoder reranking, and embedding adapters.
Show steps
Show steps
Show steps
- Gather a dataset for your retrieval system
- Design and implement the architecture of your retrieval system
- Train and evaluate your retrieval system
- Deploy your retrieval system and evaluate its performance
Career center
Learners who complete Advanced Retrieval for AI with Chroma will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/ogglnb/advanced
Reading list
We haven't picked any books for this reading list yet.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ogglnb/advanced
Share
Similar courses
Here are nine courses similar to
Advanced Retrieval for AI with Chroma.
Enhance Text Generation with RAG, LangChain, and Vertex AI
OpenCourser.com/course/86urxf/enhance
Enhance Text Generation with RAG, LangChain, and Vertex AI
Most relevant
Build a Knowledge Based System with Vertex AI Vector Search, LangChain and Gemini
OpenCourser.com/course/n0ao6l/build
Build a Knowledge Based System with Vertex AI Vector...
Most relevant
LangChain Chat with Your Data
OpenCourser.com/course/xw4o9q/langchain
LangChain Chat with Your Data
Most relevant
Prompt Compression and Query Optimization
OpenCourser.com/course/hlrtzl/prompt
Prompt Compression and Query Optimization
Most relevant
Retrieve Data using Single-Table SQL Queries
OpenCourser.com/course/jktg7y/retrieve
Retrieve Data using Single-Table SQL Queries
Most relevant
Haystack - Build customizable LLM pipelines with AI Tools
OpenCourser.com/course/7daf7p/haystack
Haystack - Build customizable LLM pipelines with AI Tools
Most relevant
Knowledge Graphs for RAG
OpenCourser.com/course/w7xbbx/knowledge
Knowledge Graphs for RAG
Most relevant
Large Language Models with Semantic Search
OpenCourser.com/course/x7jhxd/large
Large Language Models with Semantic Search
Most relevant
Introduction to SQL and relational databases
OpenCourser.com/course/2olnrz/introduction
Introduction to SQL and relational databases
Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructor
Anton Troynikov
Session
August 26, 2024
- September 9, 2024
Language
English
Good to know
Teaches advanced retrieval techniques to improve relevancy of search results, crucial for effective information retrieval and retrieval augmented generation
Instructors Anton Troynikov are seasoned experts in the field
Suitable for learners seeking to enhance their information retrieval skills
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser