Forem: Bhanu Pratap Singh

Embedding Maths for RAG

Bhanu Pratap Singh — Wed, 20 May 2026 00:33:54 +0000

Estimate chunk count, embedding storage, vector index size, and monthly database cost for your RAG knowledge base.

Your RAG Bill Isn't the LLM. It's the Embeddings: The Math Most Teams Skip — SuperML.dev

At meaningful query volume, embedding and vector DB cost routinely exceed LLM inference. Model it before you commit to a vendor — or watch re-embedding quietly dominate your bill.

superml.dev

RAG Retrieval Quality Is a Chunking Problem

Bhanu Pratap Singh — Wed, 20 May 2026 00:27:52 +0000

Your RAG Retrieval Quality Is a Chunking Problem, Not a Model Problem — SuperML.dev

Most production RAG failures trace back to chunking — the upstream decision that gets the least architectural thought. Plan chunk size, overlap, and strategy before you embed 50GB the wrong way.

superml.dev

RAG Chunking Calculator — Chunk Count, Overlap Waste & Embedding Cost | SuperML — SuperML.dev

Estimate RAG chunk count, overlap token waste, vector storage size, and embedding ingestion cost. Get recommended chunk size and overlap for your document type and strategy.

superml.dev

NL-to-SQL Complexity Calculator

Bhanu Pratap Singh — Wed, 20 May 2026 00:25:57 +0000

Assess the complexity and risk of building a natural language to SQL system over your enterprise data. Get a recommended architecture pattern and identify key risks before you build.

What the calculator actually models

Inputs:

Schema size — table count, column count
Join complexity — how many tables a typical query touches
Data freshness requirements (real-time, batch, eventually consistent)
Query diversity — narrow analytical workload vs. open-ended self-serve
Query type mix — read-only analytics vs. transactional mutations
Error tolerance — research dashboard vs. financial reporting

Outputs:

Complexity score — Low / Medium / High / Critical
Risk breakdown — retrieval errors, SQL injection via natural language, hallucinated columns
Recommended architecture — naive prompting, RAG with schema filtering, few-shot prompting, agent-based validation, hybrid
Estimated accuracy baseline for each pattern at your complexity

The most useful output is the risk breakdown. “Hallucinated columns” is the failure mode that turns into silent data corruption — the model invents a column name, the query somehow runs, and the dashboard now shows wrong numbers nobody can trace.

NL-to-SQL on a 4-Table Demo Is a Trick: How to Tell Whether You Need an Agent — SuperML.dev

The same models that score 86% on Spider 1.0 score 10-17% on real enterprise schemas. NL-to-SQL is an architecture problem, not a model problem — here's how to scope yours.

superml.dev

Calculate right class of language model for your workload

Bhanu Pratap Singh — Wed, 20 May 2026 00:14:35 +0000

Choose the right class of language model for your workload — focused on architecture, not fragile model rankings that change every week.

You're Using a Frontier Model for a Mid-Tier Task: The LLM Model Selection Calculator — SuperML.dev

Mid-tier models handle ~80% of production AI tasks at 25-35% the cost of frontier — and most teams have never benchmarked their workload to find out. Pick by task profile, not by brand.

superml.dev

Estimate the daily and monthly cost of running LLM

Bhanu Pratap Singh — Wed, 20 May 2026 00:07:26 +0000

Estimate the daily and monthly cost of running an LLM workload in production — across providers, with prompt caching and multi-model comparison.

Your LLM Bill Will 10x in Production: The Calculator That Tells You When and Why — SuperML.dev

LLM inference cost is a non-linear function of token composition, model mix, and cache behavior — and almost no team models it before shipping. Plan it before the invoice arrives.

superml.dev

LLM API Cost vs Self Host Models

Bhanu Pratap Singh — Wed, 20 May 2026 00:01:12 +0000

'We Should Self-Host' Is the Most Expensive Decision in AI: When It's Actually Right — SuperML.dev

GPU self-hosting wins on dollars-per-token at scale, but the break-even is almost always 5-20x higher than teams estimate — because they forget power, utilization, ops headcount, and quantization quality loss.

superml.dev

1M-Token Context Window Is a Lie - Plan Real Capacity

Bhanu Pratap Singh — Tue, 19 May 2026 23:54:36 +0000

Your 1M-Token Context Window Is a Lie: How to Plan Real Capacity for RAG, MCP, and Agents — SuperML.dev

The advertised context window is not the usable context window. Here's the math that decides whether your agent works in production — and the calculator that does it for you.

superml.dev

Governance Readiness Checklist for AI Architects

Bhanu Pratap Singh — Tue, 19 May 2026 23:51:47 +0000

AI governance isn't a compliance checkbox; it's a set of architectural prerequisites. The cost of retrofitting them is 5-10x the cost of designing them in. Plan before you ship.

Your AI System Will Pass Pilot and Fail Audit: A Governance Readiness Checklist for AI Architects — SuperML.dev

AI governance isn't a compliance checkbox; it's a set of architectural prerequisites. The cost of retrofitting them is 5-10x the cost of designing them in. Plan before you ship.

superml.dev

'Should We Use RAG or Fine-Tuning?' A Decision Calculator for AI Architects

Bhanu Pratap Singh — Tue, 19 May 2026 23:48:03 +0000

The single most expensive AI mistake is picking the pattern first and the problem second. Here's how to choose between RAG, GraphRAG, fine-tuning, agentic, and hybrid — by task, not by brand.

'Should We Use RAG or Fine-Tuning?' Is the Wrong Question: A Decision Calculator for AI Architects — SuperML.dev

The single most expensive AI mistake is picking the pattern first and the problem second. Here's how to choose between RAG, GraphRAG, fine-tuning, agentic, and hybrid — by task, not by brand.

superml.dev

Guide to calculate AI cost in an agent

Bhanu Pratap Singh — Tue, 19 May 2026 23:43:17 +0000

Per-task cost on agentic workflows is dominated by failure cases, not the happy path. Here's how to size retry budgets, human review, and unit economics before you ship.

Your Agent Demo Costs 4 Cents. Production Will Cost $4: The Multiplier Nobody Models — SuperML.dev

Per-task cost on agentic workflows is dominated by failure cases, not the happy path. Here's how to size retry budgets, human review, and unit economics before you ship.

superml.dev

Your AI System Will Pass Pilot and Fail Audit: A Governance Readiness Checklist for AI Architects

Bhanu Pratap Singh — Sun, 17 May 2026 05:30:05 +0000

AI governance isn't a compliance checkbox; it's a set of architectural prerequisites. The cost of retrofitting them is 5-10x the cost of designing them in. Plan before you ship.

Your AI System Will Pass Pilot and Fail Audit: A Governance Readiness Checklist for AI Architects — SuperML.dev

AI governance isn't a compliance checkbox; it's a set of architectural prerequisites. The cost of retrofitting them is 5-10x the cost of designing them in. Plan before you ship.

superml.dev

'Should We Use RAG or Fine-Tuning?' Is the Wrong Question: A Decision Calculator for AI Architects

Bhanu Pratap Singh — Sun, 17 May 2026 05:28:28 +0000

The single most expensive AI mistake is picking the pattern first and the problem second. Here's how to choose between RAG, GraphRAG, fine-tuning, agentic, and hybrid — by task, not by brand.

'Should We Use RAG or Fine-Tuning?' Is the Wrong Question: A Decision Calculator for AI Architects — SuperML.dev

The single most expensive AI mistake is picking the pattern first and the problem second. Here's how to choose between RAG, GraphRAG, fine-tuning, agentic, and hybrid — by task, not by brand.

superml.dev