NeuralNetworki.ng

20/03/2026

AI learned language without anyone telling it what a single word means.

No dictionary. No labels. No teacher.

Just billions of sentences and one instruction: figure it out.

In 1957, linguist J.R. Firth wrote —
"You shall know a word by the company it keeps."

In 2013, Word2Vec turned that into an algorithm.

The result?
Sachin ended up near Kohli.
GST ended up near tax.
Nobody programmed that.
It just... noticed.

The uncomfortable part —
it still doesn't know what words mean.
It just got very good at pretending.

Does that count as understanding?

EP 03 — Explainable by Neural Networking Systems Research Labs.

Explainable IndiaAI AIForDevelopers

19/03/2026

In 1957, linguist J.R. Firth wrote:
"You shall know a word by the company it keeps."

In 2013, Word2Vec turned that into a training algorithm.

The idea — give every word a coordinate in space based on the contexts it appears in.
Similar contexts. Similar coordinates.

To make that coordinate system intuitive:
think of Indian spices plotted by heat, tang, and fragrance.
Red chili and green chili land close.
Tamarind and raw mango cluster together.

Words do the same thing. Automatically. From data.

That coordinate is called an embedding.

Have you come across a better analogy for explaining vector space to someone non-technical?
👇 Drop it below.

EP 02 — The Spice Analogy Nobody Told You

Explainable IndiaAI AIForDevelopers

17/03/2026

We've watched developers use embeddings for months without knowing what they actually are.

They embed text. Store it in a vector DB. Call it semantic search. Ship it.

And when it breaks — they don't know why. Because they never understood what was happening underneath.

This series is for them.

Explainable EP 01 — Why Computers Can't Read.
By Neural Networking Systems Research Labs.

Explainable IndiaAI

22/01/2026

We had a potential client ask us to "just use a no-code builder" for their core product today.

We declined.

No-code is great for prototypes. It is su***de for a core product.

You cannot optimize what you cannot control. You cannot secure what is a black box.

We are Engineers. We write the code because we need to control the outcome.

We don't do "drag-and-drop" AI. We do "Systems Architecture" AI.
If you want a toy, go to Bubble.

If you want IP (Intellectual Property), come to NeuralNetworki.ng.

Call now to connect with business.

22/01/2026

𝗪𝗵𝘆 𝘄𝗲 𝗺𝗼𝘃𝗲𝗱 𝗯𝗲𝘆𝗼𝗻𝗱 𝘀𝗶𝗺𝗽𝗹𝗲 𝗥𝗔𝗚 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀

Retrieval–Augmented Generation is widely adopted.

However, in production systems with heterogeneous and entity-heavy data, vector-only retrieval often struggles with contextual precision.

We observed recurring failure modes around:

• Keyword-dominant queries
• Entity disambiguation
• Long, structured documents

To address this, we moved to hybrid retrieval architectures, combining:

• Graph-based reranking for contextual grounding
• Sparse keyword search
• Dense vector retrieval

Across internal deployments, 𝘁𝗵𝗶𝘀 𝗿𝗲𝗱𝘂𝗰𝗲𝗱 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗿𝗮𝘁𝗲𝘀 𝗯𝘆 ~𝟰𝟬% compared to naive RAG setups.

𝘙𝘈𝘎 𝘳𝘦𝘮𝘢𝘪𝘯𝘴 𝘱𝘰𝘸𝘦𝘳𝘧𝘶𝘭, 𝘣𝘶𝘵 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯 𝘢𝘤𝘤𝘶𝘳𝘢𝘤𝘺 𝘥𝘦𝘱𝘦𝘯𝘥𝘴 𝘰𝘯 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭 𝘥𝘦𝘴𝘪𝘨𝘯, 𝘯𝘰𝘵 𝘫𝘶𝘴𝘵 𝘦𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨𝘴.

We’ve created a short explainer video using NotebookLM by Google to explain how production-grade RAG systems differ from basic implementations.