Consulting essays on RAG patterns, when to fine-tune vs. prompt vs. tools, embedding drift, retrieval latency, and structured retrieval with small adapters.
The architecture choice that shapes cost, quality, and speed
How you combine a foundation model with your own data does more to determine cost, quality, and latency than which model you pick. Retrieval, fine-tuning, and tools are not competing religions; they are levers with different economics, and the skill is knowing which to pull for a given job.
These essays cover the retrieval and adaptation patterns that hold up once real data, real scale, and real users arrive.
RAG patterns that survive contact with real data
Retrieval quality, not model size, is usually the true bottleneck. Chunking strategy, hybrid search, and reranking typically do more for answer quality than swapping to a larger, costlier model, and they cost a fraction as much to change.
When to fine-tune vs prompt vs tools
Fine-tune for format and tone, prompt for reasoning, and reach for tools when the task needs ground truth or real-world actions. Most teams fine-tune too early and instrument too late, paying for both mistakes in production.
Embedding drift and retrieval latency
Embeddings age as your corpus and your models change, and retrieval latency compounds across a multi-step chain. Both are operational realities that need monitoring and periodic rebuilds, not a one-time setup you can forget.
In this collection
Essays from the Stratenity Advisory Team on foundation models and retrieval. Open any title for the full read.
Notes on chunking, grounding, and when to skip RAG entirely.
A practical decision tree we use in workshops.
End-to-end patterns for chunking, indexing, grounding, citations, and fallbacks at scale.
A lightweight drift monitor using canary queries and centroid distance.
Profiling the path: network, serialization, vector I/O, and cold caches.
Marrying structured stores with vector recall without heroics.
Go deeper with Stratenity frameworks
The public essays sketch the trade-offs. The full library holds the reference architectures, retrieval-tuning guides, and build-vs-buy diagnostics teams use to commit with confidence.
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