A First Cut RAG for Notes and Coding: Prioritizing What Matters Most

Following up on my previous post about the foundational questions and components necessary to build a Retrieval-Augmented Generation (RAG) system, I’d like to dive deeper into my personal experience creating a RAG for managing notes and coding tasks at work. Building a RAG is no small feat, and with so many factors to consider, it’s easy to feel overwhelmed. To help streamline the process, I’ve outlined the priorities that guided my development and the lessons I’ve learned along the way.

A RAG for Notes and Coding

There’s Too Much to Consider at Once

When embarking on building a RAG system, the sheer number of decisions and components can be daunting. To navigate this complexity, I focused on prioritizing key areas that would deliver the most value early on. Here are the top priorities that shaped my initial approach:

1. Focus on the End Goal

The primary objective of any RAG system is to enhance the usefulness of your LLM queries by providing relevant context. Therefore, your ultimate measure of success should be how effectively the end results meet your needs.

Key Considerations:

• Relevance: Are the retrieved facts directly related to your queries?
• Utility: Do the summaries or generated code snippets add value to your workflow?
• Specificity: Do you need the RAG to follow specific patterns or utilize approved libraries in code generation?

By clearly defining what success looks like, you can ensure that every design decision aligns with achieving that goal.

2. Establish Metrics Early

To determine whether your RAG system is improving, you need to set up metrics from the start. Initially, improvements might be obvious, but as your context becomes more refined, assessing progress can become challenging.

Steps to Establish Metrics:

• Understand Your Baseline: Know how your LLM performs without the RAG. If the LLM’s performance does not improve with additional context, something might be off.
• Define Specific Metrics:
  • LLM Performance: Measure accuracy, relevance, and coherence of generated responses.
  • Chunk Retrieval Efficiency: Assess how effectively relevant data chunks are being retrieved.
  • Response Tone and Structure: Ensure that responses maintain the desired format and tone.

Pro Tip: Save all your queries and responses. This archive will be invaluable for comparing performance over time and identifying areas for improvement.

3. Start Small

Before automating any part of the RAG process, it’s crucial to manually execute the tasks to understand their intricacies and challenges. This hands-on approach helps you gauge the system’s potential without investing significant resources upfront.

Manual Process Insights:

• Context Sourcing: Manually sourcing context from wikis, code snippets, or personal notes allows you to evaluate the quality and relevance of the data.
• Data Cleaning: I discovered that input sources like wikis often contain inconsistent statements, varying language, and opinions, which can lead to confusing and inaccurate responses. Cleaning and standardizing data is essential for reliable RAG performance.
• Code Quality: Ensuring that code snippets are well-written and consistent is critical, especially when generating new code based on existing patterns.

Starting small not only provides a clearer understanding of the data’s quality but also helps in designing effective data cleaning and preprocessing steps.

4. Know Your Limitations but Understand Your Possibilities

Every project has constraints, and being aware of them allows you to make informed decisions while also recognizing potential opportunities for growth.

Current Constraints:

• Cloud Services: I’m not yet permitted to use cloud services for storing proprietary data, which limits the tools and platforms I can leverage.
• Model Access: I have access to a single embedding model and a single LLM, which restricts the diversity of responses and retrieval capabilities.
• Data Integration: Automating data retrieval from local repositories, wikis, or communication channels like Slack isn’t feasible at the moment.

Leveraging Possibilities:

• Local Tools: Utilizing Python libraries for parsing code, chunking text, and computing vector similarity allows me to build a functional RAG within my constraints.
• Scalability Planning: Designing the system with scalability in mind ensures that it can adapt to more advanced tools and services once they become available.

Example Components:

• Data Pipeline: Currently involves manual data ingestion, such as copying data from wikis or cloning repositories. Future automation could streamline this process.
• Database: Without access to cloud-based vector indexing tools, I’m building a custom database to store chunks, metadata, and embeddings, which also serves as a learning opportunity.
• Supplemental Prompts and Prompt Templates: Handcrafted prompts help the LLM interpret data correctly and maintain consistent response formats and tones.

5. Set Priorities that Yield the Best “Bang for the Buck”

With limited resources and time, it’s essential to prioritize tasks that offer the most significant improvements to your RAG system.

Prioritization Strategy:

• Data Quality: Investing time in cleaning and standardizing your data yields substantial improvements in retrieval accuracy and response quality.
• Embedding and Retrieval Systems: Ensuring that your embedding models and chunk retrieval mechanisms are robust is critical for effective information retrieval.
• Metadata Utilization: Leveraging metadata to narrow down search results enhances performance by reducing irrelevant data retrieval, which in turn improves response accuracy.

Additional Components (Lower Priority for Now):

• Query Pre-processing: Refining how queries are handled can be addressed after the primary systems are stable.
• Versioning and Update Pipelines: Implementing version control and automated data updates are essential for scalability but can be deferred initially.
• Post-deployment User Feedback: Gathering and incorporating user feedback is valuable for iterative improvements but can be integrated once the core system is operational.

General Tips for Building Your RAG

• Begin with the Goal in Mind: Always align your design decisions with the end objectives.
• Start with a Baseline: Understand your system’s performance before adding enhancements.
• Use Multiple Metrics: Measure different aspects of performance to get a comprehensive view of your system’s effectiveness.
• Manual First, Then Automate: Gain hands-on experience before investing in automation.
• Scale Gradually: Start small to ensure quality and expand as the system proves its value.
• Ensure Data Quality: High-quality data is paramount, especially when working with smaller datasets.
• Leverage Metadata: Use metadata to enhance search precision and provide valuable context.
• Plan for the Future: Consider data versioning, automated updates, performance maintenance, and advancements in AI technologies.

What’s Next

That wraps up my series on building RAG systems! In upcoming posts, I’ll take a deep dive into the technical underpinnings of these technologies. Until then, happy building!