Emmimal P Alexander

In her latest technical walkthrough, @emmimalpa explains in great detail how she built a deterministic alternative to LLM wikis. towardsdatascience.com/llm-wikis-are-…

When vector RAG isn't delivering the results you were hoping for, @emmimalpa proposes a context graph layer — and explains on how to build it for your own pipeline. towardsdatascience.com/vector-rag-isn…

In her new deep dive, @emmimalpa outlines a practical framework to detect hidden prompt regressions before they silently break production systems. towardsdatascience.com/prompt-enginee…

What if your LLM pipeline is failing because you made it remember too much? @emmimalpa explains how to add a prompt-pruning layer to remove redundant and low-value tokens you accumulate over time. towardsdatascience.com/long-context-i…

Looking for ways to make your RAG workflow better? Emmimal P Alexander outlines her approach to adding a context layer into the system. towardsdatascience.com/rag-isnt-enoug…

In her new MLOps deep dive, Emmimal P Alexander explains why calendar-based retraining fails in production, and how a practical shock-detection approach can work in real systems. towardsdatascience.com/why-mlops-retr…

In her latest agentic-AI deep dive, Emmimal P Alexander explains how a 200-task benchmark revealed why ReAct agents waste retries — and presents the three fixes that eliminate them. towardsdatascience.com/your-react-age…

"The real problem is not retrieval — it’s what actually enters the context window. A context engine controls memory, compression, re-ranking, and token limits explicitly." Emmimal P Alexander's new deep dive explores a powerful approach for improving LLM systems. towardsdatascience.com/rag-isnt-enoug…

SHAP too slow for real-time fraud detection? (~30ms per explanation) New neuro-symbolic model generates explanations in **0.9 ms** — 33× faster, fully deterministic, inside the forward pass. @TDataScience Article: towardsdatascience.com/explainable-ai… #XAI #FraudDetection #NeuroSymbolic #AI

Tired of model drift killing your production accuracy overnight? New: Self-Healing Neural Networks in PyTorch — fix drift in real time *without* retraining or downtime.. @TDataScience Article: towardsdatascience.com/self-healing-n… #PyTorch #MLOps #AI

Read it @TDataScience → towardsdatascience.com/neuro-symbolic… Code: github.com/Emmimal/neuro-… What drift type keeps you up at night in production?

I trained a neural network to learn its own fraud rules. No hand-coded logic. Just gradient descent → IF–THEN rules. It even rediscovered a key fraud signal on its own. 👇@TDataScience towardsdatascience.com/how-a-neural-n… #AI #NeuroSymbolic #ExplainableAI