Your RAG App Keeps Missing the Right Answer. Here’s Why.

📰 Medium · AI

Learn why your RAG app misses the right answer and how to improve it, a crucial skill for AI engineers

intermediate Published 6 Jun 2026
Action Steps
  1. Analyze your RAG app's knowledge graph to identify potential issues
  2. Evaluate your app's retrieval and ranking algorithms for biases
  3. Test your app with diverse and edge-case inputs to identify weaknesses
  4. Fine-tune your app's models using relevant training data and hyperparameters
  5. Compare the performance of different RAG architectures and techniques
Who Needs to Know This

AI engineers and developers building RAG applications can benefit from understanding the common pitfalls and optimization techniques to improve their app's performance

Key Insight

💡 RAG apps can be improved by analyzing and optimizing their knowledge graphs, retrieval and ranking algorithms, and models

Share This
🤖 Is your RAG app missing the mark? Learn how to identify and fix common issues to improve its performance! #AI #RAG

Key Takeaways

Learn why your RAG app misses the right answer and how to improve it, a crucial skill for AI engineers

Full Article

Part 3 of 10, Becoming an AI Engineer Continue reading on Medium »
Read full article → ← Back to Reads

Related Videos

Does RAG relevant now? #aiwithakash #genai #llm #rag
Does RAG relevant now? #aiwithakash #genai #llm #rag
AI with Akash
🔥 Complete Semantic Caching Tutorial for Beginners | Explained in Tamil | GenAI | RAG | AI Agents
🔥 Complete Semantic Caching Tutorial for Beginners | Explained in Tamil | GenAI | RAG | AI Agents
AI with Akash
Integration with Streamlit | Explained in Tamil | RAG | AI Agents | GenAI | LLM | VectorDB | Caching
Integration with Streamlit | Explained in Tamil | RAG | AI Agents | GenAI | LLM | VectorDB | Caching
AI with Akash
10. Fuzzy Matching | Explained in Tamil | RAG | AI Agents | GenAI | LLM | Vector DB | Redis
10. Fuzzy Matching | Explained in Tamil | RAG | AI Agents | GenAI | LLM | Vector DB | Redis
AI with Akash
9. LLM call with Evaluation | Explained in Tamil | RAG | AI Agents | GenAI | LLM | Redis Cache
9. LLM call with Evaluation | Explained in Tamil | RAG | AI Agents | GenAI | LLM | Redis Cache
AI with Akash
8. Redis Implementation | Explained in Tamil | RAG | AI Agents | GenAI | LLM | VectorDB | Caching
8. Redis Implementation | Explained in Tamil | RAG | AI Agents | GenAI | LLM | VectorDB | Caching
AI with Akash