Building Reliable AI Agents Overcoming RAG Challenges

Five Common RAG Challenges Solved: Building Reliable AI Agents

Technical insights for building production-ready RAG systems that actually work

By Nina Loatina, Lead Developer Advocate at Contextual AI

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Building reliable AI agents isn't just about having the latest models—it's about solving the fundamental challenges that make or break real-world applications. After years of working with RAG (Retrieval Augmented Generation) systems and seeing teams struggle with the same issues repeatedly, I've identified five critical challenges that consistently trip up even experienced developers.

In this post, I'll walk you through these challenges and demonstrate how we've engineered solutions that move beyond vanilla RAG implementations to create truly production-ready systems.

Why RAG is Still Hard in 2025

Before diving into specific challenges, let's address the elephant in the room: why are we still talking about RAG difficulties when there are so many "buzzier" topics in AI?

The reality is that to truly leverage cutting-edge technologies like agents and agentic frameworks, you need rock-solid foundations. Errors propagate throughout systems, and no amount of sophisticated reasoning can compensate for poor information retrieval and context understanding.

Building production-level RAG requires getting multiple components right simultaneously:

**Extraction Pipeline**: Connectors, chunking, and metadata handling

**Accuracy Components**: Reranking, filtering, vector databases, and hallucination prevention

**Scaling Considerations**: Guard rails, latency optimization, entitlements, and model evaluation

Our Testing Methodology

For this analysis, we compared our Contextual AI platform against a vanilla RAG system built with open-source components:

- **Vanilla RAG**: Open-source document preprocessing, FAISS vector database, Facebook AI Semantic Search, and quantized Llama model

- **Contextual AI**: Our production-ready platform with jointly optimized components

Both systems used identical data and queries to ensure fair comparison. While this isn't perfectly apples-to-apples (our platform is paid service, though with a generous $25 free trial), it represents the reality most teams face when starting with RAG.

Challenge #1: Image-Based Queries

**The Problem**: Traditional RAG systems struggle with visual content. Images contain critical information that text-only processing simply can't capture.

**Real-World Example**: We tested both systems using lab safety chemical information sheets containing hazard pictograms—those flame, skull, and warning symbols that indicate chemical dangers.

**Vanilla RAG Performance**: When asked to "identify all products whose hazard pictograms include a flame symbol," the vanilla system hallucinated chemical names that weren't even in the dataset. It relied on parametric knowledge about flammable substances rather than analyzing the actual documents.

**Our Solution**: Contextual AI correctly identified "calcium metal" and "calcium carbide" with precise attribution showing exactly where the flame symbols appeared in the source documents. This works because our extraction pipeline includes:

- Layout analysis components

- Orchestration with multiple ML models for OCR

- Intelligent image captioning (customizable for subject matter expertise)

- Proper metadata preservation for attribution

Challenge #2: Acronym Disambiguation

**The Problem**: Documents often omit expanded forms of acronyms, and the same acronym can mean different things in different contexts (MVP = Minimum Viable Product vs. Most Valuable Player).

**Real-World Example**: Using the same chemical safety documents, we asked systems to "summarize the SDS for Benedict qualitative solution" where SDS stands for Safety Data Sheet.

**Vanilla RAG Performance**: The system retrieved random points from the document without understanding that SDS referred to the entire document structure.

**Our Solution**: Contextual AI understood that SDS meant the complete Safety Data Sheet and provided a comprehensive summary spanning multiple sections with proper citations. Our query expansion and reformulation components handle acronym disambiguation automatically.

**Advanced Example**: When asked "which chemicals are regulated by International Air Transport Association," the vanilla system failed because only the acronym "IATA" appeared in documents. Our system correctly identified the regulated chemicals by understanding the acronym context.

Challenge #3: Table Lookups

**The Problem**: Large tables present multiple challenges: chunking disrupts structure, unreliable value extraction, and lack of table-aware modeling for specific data retrieval.

**Real-World Example**: We used a 241-page table of Rotten Tomatoes movie ratings with titles, URLs, release dates, critic scores, and audience scores.

**Vanilla RAG Performance**: When asked for "movies with 100% critic score but not 100% audience score," the system fabricated ratings that didn't exist in the data.

**Our Solution**: Contextual AI correctly identified multiple movies meeting the criteria with exact citations. For the inverse query (movies with both perfect scores), it accurately reported that no such movies existed in the dataset while providing helpful context about near-misses.

**Key Technical Components**:

- Table-aware modeling during extraction

- Structured chunking that preserves relationships

- Mixture of retrievers optimized for tabular data

Challenge #4: Negation Handling

**The Problem**: Similarity search struggles with negation because embedding models often score negated statements as highly similar to positive statements. Words like "not" get lost in semantic similarity calculations.

**Demonstration**: Consider these sentences:

- Reference: "Polar bear DNA may help fight obesity"

- Paraphrase: "Polar bear study may boost fight against obesity"  

- Negation: "Polar bear DNA may not help fight obesity"

Most embedding models score the negation as more similar to the reference than the paraphrase—exactly backwards from what we need.

**Real-World Example**: Asked to "name a popular 2023 action movie that does not involve Tom Cruise," vanilla RAG systems typically fail to properly process the negation.

**Our Solution**: Contextual AI correctly identified "John Wick" (starring Keanu Reeves) as the #1 rated movie meeting all criteria. Our reranker has been specifically trained to handle negation, and our filtering components provide additional protection.

Challenge #5: Cross-Document Queries

**The Problem**: As databases scale, finding information that spans multiple documents becomes increasingly difficult. Queries requiring comparison or synthesis across sources won't find all relevant information in any single chunk.

**Real-World Example**: Using a collection of computer vision research papers, we asked for comparisons between different segmentation models mentioned across various papers.

**Vanilla RAG Performance**: The system defaulted to parametric knowledge, providing irrelevant information about spider arachnids when asked about a segmentation model called "SPIDER."

**Our Solution**: Contextual AI retrieved evidence from multiple papers and provided detailed architectural comparisons between SPIDER and OneFormer models, synthesizing information that appeared in separate documents.

**Technical Enablers**:

- Multi-hop query processing

- Query decomposition for complex requests

- Cross-document reasoning capabilities

Bonus Challenge: Attribution

Throughout all these examples, you've seen references to "citations" and "attribution." This isn't just nice-to-have—it's essential for regulated industries and system validation.

**The Challenge**: Tracking attribution requires maintaining provenance throughout the entire pipeline, from source document to generated response. Any black-box component breaks this chain.

**Our Solution**: Every response includes precise attribution with bounding boxes showing exactly where information originated. This enables human validation and builds trust in AI-generated responses.

The Architecture Behind the Solutions

What enables Contextual AI to solve these challenges where vanilla RAG fails? It comes down to three key principles:

1. Best-in-Class Components at Every Step

We maintain benchmark-beating performance across:

- Document understanding

- Unstructured retrieval  

- Structured retrieval

- Grounded generation

 2. Multi-Layered Hallucination Prevention

- **Retrieval Layer**: State-of-the-art retrieval ensures you get the right information first

- **Generation Layer**: Grounded language model trained specifically for RAG tasks

- **Validation Layer**: Groundedness checks before response generation

- **User Layer**: Attribution bounding boxes for manual verification

3. Sophisticated Query Processing

Our retrieval pipeline includes:

- Query reformulation and expansion

- Multi-turn and multi-hop capabilities

- Query decomposition for complex requests

- Combination of semantic and lexical search

- Instruction-following reranker (world's first)

- Multiple filtering and entitlement layers

Why This Matters for Your Business

These aren't academic problems—they're the difference between a demo that works in controlled conditions and a system that performs reliably in production. Every challenge we've discussed represents a category of queries your users will naturally try.

When your RAG system fails on image content, acronyms, tables, negation, or cross-document synthesis, users lose trust quickly. In regulated industries, these failures can have compliance implications. In customer-facing applications, they directly impact user experience.

 Getting Started

Just as you wouldn't train your own foundation model or build vector infrastructure from scratch, building RAG pipelines from the ground up—especially for challenging documents and queries—often isn't the best use of engineering resources.

**Options for Implementation**:

- **GUI Interface**: Quick setup for testing and prototyping

- **Full API**: Complete functionality for production integration

- **Component APIs**: Individual services (parser, reranker, generator) for custom architectures

**Use Cases We Support**:

- Q&A chat systems

- Workflow integration

- Deep research applications

- Specialized domain implementations

Conclusion

The future of AI agents depends on solving these fundamental RAG challenges today. While the problems are complex, the solutions are achievable with the right architecture and components working together.

The five challenges we've covered—image queries, acronym disambiguation, table lookups, negation handling, and cross-document synthesis—represent the core obstacles between prototype and production. By addressing them systematically, you can build AI agents that users actually trust and rely on.

Ready to see how your documents and queries perform? Try Contextual AI free at (https://app.contextual.ai) or reach out to info@contextual.ai for more information.

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*Nina Loatina is Lead Developer Advocate at Contextual AI, where she helps teams build production-ready RAG systems. She has seven years of experience in NLP and previously led data science teams at Intel Capital.*