Scaling RAG Systems: Building Robust Pipelines for Real-Time Performance
A RAG system that works perfectly in a local environment can often break down when pushed to production. Why? Because it wasn’t designed with real-world scenarios and constraints in mind. Enter Advanced RAG Pipelines! (Yes, I know – another buzzword. But bear with me, and I’ll break it down for you.)
And even when a RAG system works, accuracy can plummet, and the responses to queries might become completely irrelevant. Why? Poor phrasing of retrieval queries or bad sources are often to blame. (There’s even a word for it - GIGO (Garbage In Garbage Out) which means poor inputs gives poor results). Some techniques to increase the accuracy are discussed below.
Query Rewriting
It is the process of modifying or reformulating user queries to improve the quality and relevance of the results retrieved by a system. It involves techniques like expanding, rephrasing, or simplifying the original query to make it more specific or contextually relevant.
Need for Query Rewriting:
Improves Retrieval Accuracy: Helps in retrieving more relevant results by clarifying or broadening the query intent.
Handles Ambiguities: Reduces misunderstanding or vagueness in user queries.
Optimizes Resource Use: Enhances search efficiency by narrowing down the search scope to more precise results.
LLM as a judge
Using a Large Language Model (LLM) as a judge refers to utilizing the model to evaluate or assess the quality, relevance, or correctness of outputs generated by a system, typically after it has performed some retrieval and generation task (such as in a RAG system).
How LLM Can Act as a Judge:
Evaluating Generated Content:
The LLM can be used to assess the coherence, relevance, and factual accuracy of a generated response. After a query is processed and a response is generated by the model, the LLM can "judge" if it aligns well with the expected output.For example:
Coherence Check: The LLM checks if the response is logically consistent and coherent.
Relevance Check: The LLM assesses whether the response directly addresses the query.
Factual Check: The LLM may check whether the response provides accurate and factual information.
Ranking Responses:
In a system where multiple responses are generated or retrieved, the LLM can rank these responses based on quality. It may prioritize responses that are more relevant, informative, or well-structured.Self-Improvement:
In some scenarios, the LLM may be part of an iterative process where it judges its own output. If a response doesn't meet quality standards, the LLM can be prompted to reformulate or generate alternatives.Scoring Mechanism:
The LLM can be used to assign a score to responses, which can be used as part of a feedback loop to improve performance. For instance, after generating a response, the LLM evaluates it on a scale (e.g., 1-5) and helps identify areas where the system can improve.Example Use Case:
Imagine a customer service chatbot that uses an LLM to answer queries. The system generates a response to a user's inquiry, but before presenting it to the user, the LLM is used to evaluate:
Does the answer make sense?
Is it relevant to the user’s query?
Does it align with the company’s policies or guidelines?
If the response is judged to be inaccurate or unclear, the system can either modify the response or generate a new one.
Add more context to the user query
Using a Large Language Model (LLM) to provide relevant content for user queries can enhance accuracy, but it often leads to hallucinations—incorrect or fabricated information. To prevent hallucinations while improving accuracy, a ranking system can be applied.
How It Works:
Generate Multiple Responses: Instead of a single response, generate several possible answers from the LLM.
Rank Responses: Evaluate responses based on:
Relevance: How closely it matches the query.
Accuracy: Cross-check factual correctness.
Coherence: Logical consistency of the response.
Select the Best Response: Choose the highest-ranked response that is relevant, factually accurate, and coherent.
Benefits:
Prevents Hallucinations: Ranking helps discard incorrect responses.
Improves Accuracy: Selecting the best answer ensures a more accurate response.
Increases Reliability: By cross-checking and ranking, the system provides more dependable content.
This approach combines LLMs' ability to generate rich, contextual content with a safeguard to prevent errors and ensure quality responses.
HyDE
Hypothetical Document Embeddings helps improve query relevance when a user’s query lacks specificity. Here’s how it works:
User Query is Vague: When a query lacks clarity, the LLM generates a hypothetical answer based on its understanding of the domain.
Embedding Creation: The hypothetical answer is converted into an embedding.
Accurate Retrieval: This embedding allows the system to retrieve more relevant chunks of information that align with the user's implied intent.
Benefits:
Improves Retrieval: Helps the system fetch more contextually relevant data.
Handles Vague Queries: Even unclear queries lead to more accurate responses.
Example:
Query: "Tell me about climate change."
Hypothetical Answer: "Climate change is caused by human activities like burning fossil fuels and deforestation, leading to global warming."
Result: The system retrieves more accurate and relevant documents on climate change causes.
HyDE allows the system to generate contextually accurate answers by using hypothetical embeddings for better retrieval.
Speed v/s Accuracy Tradeoff
The speed vs accuracy trade-off is like this:
Accuracy Focus: You take your time, compute the right answer, and get the most relevant result—but it’s slower.
Speed Focus: You rush for the answer, get it instantly, but the result might be off.
Example:
If you ask me "What’s 50 + 70?" and I immediately tell you "200", I was definitely fast, but I’m not winning any math awards. I mean, I could have said "300" and saved you a whole 0.5 seconds! 😅
I was quick, but... the math department is calling, and they’re not happy. 😬
Strategies:
Caching: Save common data to speed things up while keeping accuracy intact.
Approximate Search (ANN): Faster searches with minimal accuracy loss.
Model Simplification: Use lighter models for speed, heavier ones for accuracy.
Batch Processing: Handle multiple queries together for optimized performance.
In short, for real-time systems, speed wins, but for critical systems, accuracy takes the crown.
Wrapping up!
Your first RAG application may work flawlessly in a controlled environment, but once it's pushed into production and subjected to real-time loads, challenges arise. This is where well-designed pipelines come into play.
