Newsletter August 2025 Issue: Not Just Reasoning, But Retrieval, Is the Biggest Challenge of Building Modern AI

When we talk about advancing modern AI, especially Agentic RAG (Retrieval-Augmented Generation), most attention goes to reasoning — chain-of-thought improvements, tool use, and planning. But in practice, the largest bottleneck is retrieval: how we find the right information for the model to reason over in the first place. Even the most capable LLM will hallucinate or misinterpret if the context is incomplete, irrelevant, or poorly ranked. This is why retrieval design — not just reasoning — has become the real differentiator in production-grade AI systems.

Let's take a look at why. In real life scenarios, platform companies generate hundreds, thousands or millions of data points per day. When you build agentic AI that can reason over such data, you have to solve the retrieval bottleneck before you can even think about reasoning quality. Your system needs to locate the most relevant, trustworthy, and contextually rich information fast — often in milliseconds — and feed it into the LLM in a way that preserves meaning. That’s where a well-designed retrieval layer becomes the backbone of your agentic RAG pipeline.

Modern Retrieval Methods

The retrieval method you will use will be highly dependent on the kind of data the AI system has to work on. Is most of your data stored in documents? Are they in SQL databases? Do you need systems that can reason over interconnected data? These questions become pertinent the moment you start building systems that will work in production.

In many cases, you may need to combine multiple retrieval methods instead of relying on a single search call. Below, we have listed some of the most common retrieval methods in use today:

Exact Match Filters – Apply metadata filters, SQL conditions, or RBAC (Role-Based Access) rules before retrieval to narrow the dataset.
BM25 Keyword Retrieval – Retrieve documents based on exact term matches for precision-critical queries.
Vector Similarity Search (k-NN / ANN) – Use embeddings to find semantically similar chunks via cosine similarity, dot product, etc.
Hybrid Search – Combine keyword and vector search for balanced precision and recall.
Multi-Vector Retrieval – Store multiple embeddings per chunk (title, summary, facts) to match different query intents.
Dense + Sparse Fusion – Merge dense semantic vectors with sparse term-based scores (e.g., ColBERT, SPLADE).
Graph Retrieval – Traverse knowledge graphs to surface entities, relationships, and paths that connect concepts, enabling reasoning over structured relationships instead of flat text alone.
Reranking Models – Retrieve a broad set, then rerank with cross-encoders or LLMs for better relevance.
Agent-Orchestrated Iterative Retrieval – Let the agent reformulate queries, apply filters, and chain searches mid-task.
Contextual Retrieval Chains – Adapt queries using conversation history or intermediate tool outputs.
Domain-Adaptive Embeddings – Train embeddings on your industry’s terminology for better domain-specific matching.

When you pick the right retrieval methods, you enable your AI to behave more like an informed agent, continuously adapting its search strategy as it reasons. This combination of adaptive retrieval + advanced reasoning is what separates a toy chatbot from a production-ready intelligent system.

Can Frameworks Help?

Frameworks can definitely give you a head start. Many popular RAG and agent orchestration frameworks — LangChain, LlamaIndex, Haystack, Semantic Kernel — provide prebuilt connectors, vector store integrations, and query pipelines. If you’re working with a small dataset, standard document formats, and predictable retrieval patterns, these tools can save you weeks of development time. They abstract away the plumbing so you can quickly prototype and validate your agent’s reasoning loop.

But as soon as you move into production with domain-specific data, the cracks start to show. Each domain has unique data quirks:

In legal tech, you’re working with highly structured clauses and references that demand exact-match and citation-aware retrieval.
In manufacturing, you’re dealing with real-time sensor streams, maintenance logs, and CAD metadata that require hybrid temporal + semantic search.
In finance, compliance rules and time-bound datasets mean retrieval has to respect regulatory cut-offs and audited metadata.
In healthcare, patient data lives across EHR systems, medical imaging repositories, and research papers — each requiring different retrieval strategies and privacy enforcement.

Frameworks, by design, aim for generalized abstractions. That’s their strength in prototyping — and their weakness in scaling. They can hide too much of the retrieval complexity, making it difficult to:

Implement multi-index retrieval across different data modalities.
Tune retrieval pipelines for latency-sensitive queries at scale.
Apply domain-adaptive embeddings without breaking internal data flow.
Inject agent-led query reformulation loops that don’t fit the framework’s orchestration model.

At some point, you’ll find yourself writing more custom connectors, pre-processors, and rerankers than the framework’s abstractions were designed to handle. That’s when you need to start treating retrieval not as a “black box” module but as a first-class engineering problem — built for your domain, your latency targets, and your accuracy requirements.

The Right Approach

Instead of forcing your system to fit a framework’s limitations, flip the approach: build the retrieval flow you need, then selectively use framework components where they add speed without blocking flexibility. This way, you keep control over query reformulation, index selection, and ranking logic, while still benefiting from prebuilt tooling when it makes sense.

In production, the most successful agentic RAG systems are not those that rely solely on the biggest LLMs, but those that pair sharp, adaptive retrieval pipelines with capable reasoning loops. When your AI can consistently find the right context, it stops guessing and starts delivering results you can trust. Retrieval isn’t the supporting act — it’s half the intelligence in your intelligent system.

Happy building!

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