Perivitta Rajendran

Mar 21, 2026

Advanced Prompt Engineering: Chain-of-Thought, ReAct, and Tree-of-Thoughts Explained

Chain-of-thought improves multi-step reasoning. ReAct adds tool use. Tree-of-thoughts explores multiple solution paths. When each technique earns its token cost — and...

blogpost· 24 mins read

Mar 19, 2026

blogpost

Structured Outputs in LLMs: JSON Mode, Function Calling, and Schema Validation

Free-form LLM output breaks parsing pipelines. JSON mode, function calling, grammar-constrained decoding, and Pydantic validation are the layers that make structured output...

blogpost· 20 mins read

Mar 14, 2026

blogpost

Prompt Injection Attacks: How LLMs Get Exploited and How to Defend Your Application

Prompt injection turns user input into an instruction override. Indirect injection, jailbreaks, and data exfiltration vectors are all in scope — and...

blogpost· 23 mins read

Mar 12, 2026

blogpost

LLM Observability: Tracing, Logging, and Debugging AI Applications

You can't debug what you can't trace. Setting up prompt logging, span tracing, cost tracking, and latency monitoring for production LLM apps...

blogpost· 19 mins read

Mar 5, 2026

blogpost

Hybrid Search: Combining Keyword and Vector Search for Better Retrieval

Pure vector search misses exact matches. BM25 misses semantic intent. Reciprocal rank fusion combines both without the tuning overhead of learned fusion...

blogpost· 19 mins read

Feb 27, 2026

blogpost

PEFT Methods Explained: LoRA, QLoRA, and Adapter-Based Fine-Tuning

Full fine-tuning a 7B model costs thousands in GPU hours. LoRA and QLoRA achieve comparable quality by training a fraction of the...

blogpost· 21 mins read

Feb 22, 2026

blogpost

Why Your LLM Application Feels Slow

LLM latency usually isn't the model's fault. Synchronous retrieval, sequential tool calls, missing streaming, and cold-start overhead are the architectural decisions that...

blogpost· 14 mins read

Feb 20, 2026

blogpost

A Beginner's Guide to Cost Optimization in LLM Applications

LLM API costs compound fast at scale. Token budgeting, model routing, prompt caching, and batching are the four levers that cut costs...

blogpost· 15 mins read

Feb 19, 2026

blogpost

How Retrieval-Augmented Generation (RAG) Works

RAG grounds LLM responses in retrieved documents rather than model weights. Walk through the full pipeline — indexing, retrieval, augmentation, and generation...

blogpost· 17 mins read

Feb 18, 2026

blogpost

What is an AI Agent?

An LLM becomes an agent when it can reason about which tool to call, execute that call, and update its plan based...

blogpost· 17 mins read

Perivitta Rajendran

Recent writing

Multi-Agent Systems: Orchestration, Communication, and Collaborative AI

LLM Inference Optimization: Quantization, KV Cache, and Serving at Scale

Embedding Models: Training, Fine-Tuning, and Optimization for Retrieval

From the archive

Advanced Prompt Engineering: Chain-of-Thought, ReAct, and Tree-of-Thoughts Explained

Structured Outputs in LLMs: JSON Mode, Function Calling, and Schema Validation

Prompt Injection Attacks: How LLMs Get Exploited and How to Defend Your Application

LLM Observability: Tracing, Logging, and Debugging AI Applications

Hybrid Search: Combining Keyword and Vector Search for Better Retrieval

PEFT Methods Explained: LoRA, QLoRA, and Adapter-Based Fine-Tuning

Why Your LLM Application Feels Slow

A Beginner's Guide to Cost Optimization in LLM Applications

How Retrieval-Augmented Generation (RAG) Works

What is an AI Agent?

Recent writing

Multi-Agent Systems: Orchestration, Communication, and Collaborative AI

LLM Inference Optimization: Quantization, KV Cache, and Serving at Scale

Embedding Models: Training, Fine-Tuning, and Optimization for Retrieval

Not sure where to start? Just ask.

From the archive

Advanced Prompt Engineering: Chain-of-Thought, ReAct, and Tree-of-Thoughts Explained

Structured Outputs in LLMs: JSON Mode, Function Calling, and Schema Validation

Prompt Injection Attacks: How LLMs Get Exploited and How to Defend Your Application

LLM Observability: Tracing, Logging, and Debugging AI Applications

Hybrid Search: Combining Keyword and Vector Search for Better Retrieval

PEFT Methods Explained: LoRA, QLoRA, and Adapter-Based Fine-Tuning

Why Your LLM Application Feels Slow

A Beginner's Guide to Cost Optimization in LLM Applications

How Retrieval-Augmented Generation (RAG) Works

What is an AI Agent?