{"id":9,"title":"What is Retrieval Augmented Generation aka RAG?","description":"Retrieval-augmented generation (RAG) is an AI framework used to enhance the accuracy and reliability of generative AI models with facts fetched from external sources.","datepublished":"2024-08-14T00:00:00.000Z","readingtime":"7","creator":"Zubair Altaf","content":"

Scenario:

Imagine a busy hospital where doctors diagnose and treat a wide range of medical conditions. Just like good doctors, large language models (LLMs) can rely on their medical training and experience to make informed decisions. However, for complex, rare conditions or new diseases, doctors often need to reference the latest medical research, treatment guidelines, and case studies is a process called retrieval-augmented generation, or RAG for short in AI.

Defination:

Retrieval-augmented generation (RAG) is a proccess of optimizing the output of a large language model (LLM), providing up-to-date, near to reality and generic information.

Flaws in LLM:

Prompt injection:

It's a common risk where a user can control the prompts given to a Large Language Model (LLM). If these prompts aren't carefully managed, the LLM might produce content that doesn't match the intended guidelines.

Biased Information:

LLMs are often trained on large datasets that may contain biases present in the text. This can lead to models generating biased or unfair content.

False Information:

LLM can produce fake responses as LLM is capable to fabricate the text that closely matches the user input query which can be wrong according to what user wants.

Outdated Information:

Providing information that is no longer relevant or has been superseded by new data.

Absence of responsibility

It's hard to figure out who is responsible for the content (LLM) produce. This is a big deal in legal and ethical situations.

Benefits of integrating RAG within LLM

Building User Trust

Retrieval-augmented generation (RAG) helps models provide accurate information with citations, similar to footnotes in a research paper, enhancing user trust.

For example,

User: What are the benefits of regular exercise?

RAG: Regular exercise offers numerous benefits backed by scientific research. According to sources like the American Heart Association and the Mayo Clinic, exercising regularly can improve cardiovascular health, boost mood, reduce the risk of chronic diseases such as diabetes and hypertension, enhance cognitive function, and promote better sleep. These benefits are well-documented and supported by a wealth of reliable evidence, ensuring the information provided is trustworthy and accurate.

Improved Precision and Reliable Information

In contrast to conventional language models, Retrieval-Augmented Generation (RAG) uses external, verifiable sources to inform its responses. This approach significantly minimizes the likelihood of producing \"hallucinations\" or erroneous content. As a result, the outputs are more trustworthy and reliable.

For example,

User: Can you tell me about the capital of France?

RAG: Sure! The capital of France is Paris. It's known for its iconic landmarks like the Eiffel Tower and the Louvre Museum.

Near to Human-Like and Natural Sounding:

By seamlessly blending factual information with creative generation, RAG can produce outputs that are more engaging and natural-sounding, closer to how humans communicate.

For example,

User: What is my income potential as a Javascript developer in Texas?

RAG: Ah, diving into the world of JavaScript development in Texas! Well, according to various sources like Glassdoor and Indeed, the income for JavaScript developers in Texas can vary based on factors like experience, location within Texas, and the specific company you work for. On average, however, junior developers might start around $60,000 to $80,000 per year, while mid-level and senior developers can earn anywhere from $90,000 to $150,000 annually, or even more at top tech companies or in specialized roles. Of course, these figures are just averages, and your actual income could vary based on your skills, experience, and negotiation prowess.

Cutting Down on Training Time and Cost Efficient

Traditional models need to be trained a lot, using big sets of data, and they need to be updated often. But with RAG, it can learn from new sources easily, so we don't have to train it as much or update it as often. This saves a lot of time and money.

For example,

User: How does RAG differ from traditional models in terms of training time and data requirements?

RAG: Unlike traditional models that require extensive training with large datasets and frequent updates, RAG operates differently. It can quickly adapt and learn from new sources, significantly reducing the need for extensive training and frequent updates.

How Rag Works

RAG is the key to augement the LLM response that is created against user input it was trained on. RAG has introduced the retrieval process that first gets the upto date data for user input from external and upto date sources and mediums. Then data is passed to LLM with new results and that is added it to LLM to create authentic, upto date responses. Bellow are the sections how this proccess works with RAG in LLM.

Getting Data From External Sources

In first step user input goes to external sources through RAG. External sources can come from API's, Articles, Databases, files. Data can be in any formats as here the only required process is to get maximum related data from external sources. This is the step that gives the large data set that Generative AI can understand easily.

Relevance-Based Search in Smart Chatbots:

The user query is next converted to vector and matched with data stored in databases recently fetched from external resources

For example, in an educational setting, a student querying \"How do I solve quadratic equations?\" could prompt retrieval of relevant resources like textbooks, lecture notes, and past solved examples tailored to their academic level. Here is the point where Generative AI comes in mid that uses mathematical vector calculations to reproduce more relavent information

Enhance the LLM prompt

The RAG model makes user questions better by adding extra information that helps the computer understand more. This extra info comes from previous knowledge and helps the computer give better answers. So, when you ask something, the computer can use all this extra knowledge to give you a really good answer. This augmented prompt makes the LLM to generate more precise responses.

Refreshing External Data

Ensuring that external data remains relevant is crucial for maintaining the accuracy and effectiveness of any data analytics system. When external data becomes stale, it can lead to outdated insights and decisions based on obsolete information. To address this challenge, implementing processes for asynchronous updates of documents and their corresponding embedding representations is essential.

Conclusion

In summary, by adding up-to-date information from external sources, retrieval-augmented generation (RAG) makes computer-generated responses more accurate and trustworthy. It fixes problems like giving wrong or old information and helps users trust the answers more. Plus, it makes the responses sound more natural, like how people talk. Overall, RAG is a big step forward in making computers better at understanding and talking to us.

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