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Balaji Natarajan
Balaji Natarajan
Idempotent tools: making agent retries safe

Oct 25, 2026

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2 min read

Idempotent tools: making agent retries safe

A timeout doesn't mean the call failed, so build tools that can run twice without doing damage.

Balaji Natarajan
Balaji Natarajan
Approval gates: put them where the risk is

Oct 18, 2026

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4 min read

Approval gates: put them where the risk is

Ask a human only about actions you can't undo, and the approvals will still get read.

Balaji Natarajan
Balaji Natarajan
Control plane vs data plane, the half of a managed service nobody demos

Oct 14, 2026

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9 min read

Control plane vs data plane, the half of a managed service nobody demos

Creating, metering, isolating and upgrading 2,000 vector store clusters is a bigger build than the engine, and here is the order to build it.

Balaji Natarajan
Balaji Natarajan
Where your GPU memory goes: training vs inference

Oct 9, 2026

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8 min read

Where your GPU memory goes: training vs inference

Four buckets, one 8B model, one 24GB card: how to estimate what fits before you launch, and which lever shrinks which bucket.

Balaji Natarajan
Balaji Natarajan
Parameter-efficient fine-tuning compared: what LoRA and its relatives trade off

Oct 8, 2026

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6 min read

Parameter-efficient fine-tuning compared: what LoRA and its relatives trade off

A memory, quality and complexity comparison of LoRA, QLoRA and (IA)3, plus a rule for picking one under a fixed GPU budget.

Balaji Natarajan
Balaji Natarajan
Shrinking an agent's history: trimming, summarising and retrieval

Oct 6, 2026

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7 min read

Shrinking an agent's history: trimming, summarising and retrieval

Trim, summarise or retrieve: three ways to cut what a support agent resends every step, and what each one actually saves.

Balaji Natarajan
Balaji Natarajan
Prompt caching for agent loops: what it changes about cost per run

Oct 4, 2026

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6 min read

Prompt caching for agent loops: what it changes about cost per run

Splits cost into fresh, cache-write and cache-read tokens on a PR-review run, and shows when caching backfires.

Balaji Natarajan
Balaji Natarajan
Same regression models, different metric: how MAE vs RMSE reorders the leaderboard

Oct 2, 2026

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7 min read

Same regression models, different metric: how MAE vs RMSE reorders the leaderboard

Score the same four fitted models under MAE and RMSE on one housing dataset, and find that 3% of the test set, the priciest homes, decides which of two closely matched tree ensembles wins, while a linear model and a robust regressor never contend under either metric.

Balaji Natarajan
Balaji Natarajan
Put a meter on your agent: what one run actually costs

Sep 29, 2026

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8 min read

Put a meter on your agent: what one run actually costs

Wrap the model call, count tokens per step, and turn one run into a cost estimate you can take to leadership.

Balaji Natarajan
Balaji Natarajan
Build the dumbest model first: a 10-minute baseline that saves weeks

Sep 27, 2026

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7 min read

Build the dumbest model first: a 10-minute baseline that saves weeks

Build a majority-class and a mean baseline in scikit-learn, then use them to decide if the real model is worth shipping.

Balaji Natarajan
Balaji Natarajan
Choose a metric by what a mistake costs you

Sep 25, 2026

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6 min read

Choose a metric by what a mistake costs you

Two fraud models tie at 99.5% accuracy, and only one is useful. Ask what each mistake costs to pick the metric.

Balaji Natarajan
Balaji Natarajan
Fine-tuning is a poor way to teach a model new facts

Sep 22, 2026

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6 min read

Fine-tuning is a poor way to teach a model new facts

A fine-tuned returns bot keeps quoting the old 30-day window; here is a rule for what belongs in weights versus prompts.

Balaji Natarajan
Balaji Natarajan
What Pydantic actually buys you over a dataclass (and when to reach for it)

Sep 20, 2026

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6 min read

What Pydantic actually buys you over a dataclass (and when to reach for it)

Type hints don't check LLM output at runtime. Here's how one Pydantic model catches a bad ticket at the door instead of three files later.

Balaji Natarajan
Balaji Natarajan
Four prompting techniques you actually need (and when to use each)

Sep 18, 2026

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3 min read

Four prompting techniques you actually need (and when to use each)

Clear instructions, few-shot examples, chain-of-thought and role prompting: what each one fixes, and how to pick the right one for the task.

Balaji Natarajan
Balaji Natarajan
Building RAG: The Old Way vs. The LCEL Way

Sep 16, 2026

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3 min read

Building RAG: The Old Way vs. The LCEL Way

Why the deprecated RetrievalQA chain hides too much, and how piping small LCEL pieces gives you streaming, parallel retrieval, and debuggability.

Balaji Natarajan
Balaji Natarajan
Part - 3: The SaaS AI Scale-Up: The Model Factory

Sep 4, 2026

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3 min read

Part - 3: The SaaS AI Scale-Up: The Model Factory

Turn fine-tuning from a manual research event into a CI/CD pipeline, and every customer correction becomes a moat that a bigger generalist model can't copy.

Balaji Natarajan
Balaji Natarajan
Part - 2: The SaaS AI Scale-Up: The Multi-Tenant Architecture

Sep 1, 2026

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3 min read

Part - 2: The SaaS AI Scale-Up: The Multi-Tenant Architecture

One shared 8B base plus a 50MB LoRA adapter per customer gives you 1,000 specialists without 1,000 GPUs.

Balaji Natarajan
Balaji Natarajan
Part-1: The SaaS AI Scale-Up: The Task-Specific Specialist

Aug 30, 2026

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3 min read

Part-1: The SaaS AI Scale-Up: The Task-Specific Specialist

Traditional ML can't read a demo request, and a 70B LLM is too slow and costly to fix it. A fine-tuned 8B specialist can.

Balaji Natarajan
Balaji Natarajan
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