DEV Community: Rajesh Mishra

RAG Retrieval Augmented Generation Complete Tutorial 2026

Rajesh Mishra — Sun, 11 Oct 2026 14:29:28 +0000

This is a summary of the full tutorial published on howtostartprogramming.in.

TL;DR Retrieval‑Augmented Generation (RAG) combines a vector store (or traditional search engine) with a generative LLM so the model can ground its answers in up‑to‑date, domain‑specific data. In 2026 RAG is the de‑facto pattern for: Enterprise knowledge‑base assistants that stay compliant with the latest policies. Multimodal agents that retrieve text, images, or code snippets before responding. Cost‑effective LLM usage – the model only generates, not memorises, large corpora. Key steps to a production‑ready RAG system (2026) : Step What to do Typical tools (2026) 1⃣ Data Ingestion Collect, clean, and chunk documents (text, PDFs, code, images). Unstructured.io , LangChain DocumentLoaders , Apache Tika 2⃣ Embedding & Indexing Generate dense embeddings and store them in a scalable vector DB.

📖 Read the Full Tutorial

🔗 RAG retrieval augmented generation complete tutorial 2026 — Full Guide with Code Examples

The full article includes:

✅ Step-by-step code examples (copy-paste ready)
✅ Complete working project (Spring Boot / Java)
✅ Common mistakes + fixes
✅ Production tips and benchmarks
✅ FAQ section

Published on How to Start Programming — practical AI and Java tutorials for developers.

Structured Output with JSON Mode: OpenAI and Groq Tutorial 2026

Rajesh Mishra — Fri, 09 Oct 2026 14:47:21 +0000

This is a summary of the full tutorial published on howtostartprogramming.in.

TL;DR A fast‑track guide to getting reliable, structured JSON output from OpenAI’s json_mode using Groq. Set up, core concepts, and production‑ready tips—all in one glance. Step What to Do Why It Matters 1⃣ Install SDKs pip install openai groq Provides the client libraries for both APIs. 2⃣ Enable JSON mode Set response_format={"type":"json_object"} in the OpenAI request. Forces the model to emit strict JSON, reducing parsing errors. 3⃣ Define a schema Use a JSON Schema or a simple pydantic model to describe the expected shape. Gives the model a concrete contract and lets Groq validate the output. 4⃣ Pipe through Groq Send the raw JSON to groq.validate() (or a custom validator) before consumption. Catch structural mismatches early, especially in production pipelines. 5⃣ Production tips Wra

📖 Read the Full Tutorial

🔗 Structured output with JSON mode OpenAI and Groq tutorial 2026 — Full Guide with Code Examples

The full article includes:

✅ Step-by-step code examples (copy-paste ready)
✅ Complete working project (Spring Boot / Java)
✅ Common mistakes + fixes
✅ Production tips and benchmarks
✅ FAQ section

Published on How to Start Programming — practical AI and Java tutorials for developers.

Spring Batch read CSV file and write to database example — Complete Guide

Rajesh Mishra — Thu, 08 Oct 2026 14:23:01 +0000

Spring Batch read CSV file and write to database example — Complete Guide

A practical, in-depth guide to Spring Batch read CSV file and write to database example with examples.

INTRO

Every day, enterprises receive massive CSV dumps from partners, legacy systems, or IoT devices. Turning those flat files into relational data isn’t as trivial as “read‑line‑by‑line and INSERT”. You quickly run into performance bottlenecks, transaction management headaches, and error‑handling nightmares. The classic “read whole file into memory then bulk insert” works for a few hundred rows, but it collapses under real‑world loads of millions of records.

Spring Batch was built to solve exactly this class of problems. It gives you a declarative, fault‑tolerant pipeline that can chunk data, retry failed records, and parallelize work without you having to reinvent the wheel. Yet, many developers still stare at the documentation and wonder how to wire a CSV reader to a JDBC writer in a clean, testable way. That’s the gap this teaser aims to fill.

In the full guide you’ll see a production‑ready configuration that reads a CSV with custom delimiters, maps each line to a POJO, validates the data, and writes it to a relational table using batch inserts. The example is deliberately minimal so you can copy‑paste it into a Spring Boot project, but it also dives deep into the knobs you’ll need to turn when you move from a demo to a production job.

WHAT YOU'LL LEARN

How to configure FlatFileItemReader with a DelimitedLineTokenizer and a BeanWrapperFieldSetMapper for clean POJO mapping.
Setting up JdbcBatchItemWriter with named parameters and a configurable batchSize to maximize DB throughput.
Defining a Step with chunk‑oriented processing, including skip‑policy and retry logic for malformed rows.
Building a Job that can be launched via Spring Boot, the command line, or a scheduler like Quartz.
Strategies for handling large files: multi‑threaded steps, partitioning, and resource cleanup.
Common pitfalls (e.g., transaction boundaries, CSV encoding issues) and how to avoid them.

A SHORT CODE SNIPPET

@Configuration
@EnableBatchProcessing
public class CsvToDbJobConfig {

@Bean
public FlatFileItemReader<Person> reader() {
return new FlatFileItemReaderBuilder<Person>()
.name("personItemReader")
.resource(new ClassPathResource("people.csv"))
.delimited()
.names("firstName", "lastName", "email")
.fieldSetMapper(new BeanWrapperFieldSetMapper<>() {{
setTargetType(Person.class);
}})
.build();
}

@Bean
public JdbcBatchItemWriter<Person> writer(DataSource dataSource) {
return new JdbcBatchItemWriterBuilder<Person>()
.itemSqlParameterSourceProvider(new BeanPropertyItemSqlParameterSourceProvider<>())
.sql("INSERT INTO person (first_name, last_name, email) VALUES (:firstName, :lastName, :email)")
.dataSource(dataSource)
.assertUpdates(false)
.build();
}

@Bean
public Step csvToDbStep(StepBuilderFactory stepBuilderFactory,
FlatFileItemReader<Person> reader,
JdbcBatchItemWriter<Person> writer) {
return stepBuilderFactory.get("csvToDbStep")
.<Person, Person>chunk(500)
.reader(reader)
.processor(item -> item) // no-op processor, placeholder for validation
.writer(writer)
.faultTolerant()
.skipPolicy(new AlwaysSkipItemSkipPolicy())
.build();
}

@Bean
public Job importUserJob(JobBuilderFactory jobBuilderFactory, Step csvToDbStep) {
return jobBuilderFactory.get("importUserJob")
.incrementer(new RunIdIncrementer())
.flow(csvToDbStep)
.end()
.build();
}
}

KEY TAKEAWAYS

Chunk‑oriented processing is the heart of Spring Batch; it lets you balance memory usage and DB round‑trips by controlling the chunk size.
Fault tolerance (skip, retry, and rollback) should be configured early; it prevents a single bad row from killing an entire run.
Declarative configuration using the builder API keeps the job definition readable and testable, while still exposing all the low‑level knobs you might need later.
Scalability isn’t an afterthought—Spring Batch provides partitioning and multi‑threaded steps that can be added with a few extra beans when your CSV grows beyond a few hundred thousand rows.

👉 Read the complete guide with step-by-step examples, common mistakes, and production tips:

Spring Batch read CSV file and write to database example — Complete Guide

RAG vs Fine‑Tuning vs Prompt Engineering: Choosing the Right Strategy in 2026

Rajesh Mishra — Wed, 07 Oct 2026 14:52:54 +0000

This is a summary of the full tutorial published on howtostartprogramming.in.

TL;DR – RAG vs. Fine‑Tuning vs. Prompt Engineering (2026) Pick the right strategy in three steps: Ask yourself: Do you need up‑to‑date factual grounding or domain‑specific nuance ? Check data constraints: Is the knowledge source static, streaming, or proprietary? Consider resources: Budget, latency, and maintenance overhead. Technique When to Use Pros Cons Typical Cost (2026) Retrieval‑Augmented Generation (RAG) • Need real‑time or frequently updated facts • Large external corpus (docs, web, DB) • Low tolerance for hallucinations • Fresh, source‑traceable answers • No model weight changes • Scales with corpus size • Retrieval latency adds overhead • Requires robust indexing & relevance tuning • May need custom retrievers for multimodal data Low‑to‑medium (compute + vector store) Fine‑Tunin

📖 Read the Full Tutorial

🔗 RAG vs fine tuning vs prompt engineering when to use which 2026 — Full Guide with Code Examples

The full article includes:

✅ Step-by-step code examples (copy-paste ready)
✅ Complete working project (Spring Boot / Java)
✅ Common mistakes + fixes
✅ Production tips and benchmarks
✅ FAQ section

Published on How to Start Programming — practical AI and Java tutorials for developers.

System Prompts Explained: How to Write Effective System Prompts in 2026

Rajesh Mishra — Mon, 05 Oct 2026 16:33:59 +0000

This is a summary of the full tutorial published on howtostartprogramming.in.

TL;DR: System Prompts Explained & How to Write Effective Ones (2026) Why they matter : The system prompt is the brain‑stem of any LLM interaction. It sets the model’s persona, constraints, and goals, steering output quality, safety, and consistency across sessions. Key Principle What to Do Why It Works Clear Role Definition State the model’s identity and purpose in one sentence. Reduces ambiguity; the model knows its “job”. Explicit Constraints List limits (tone, length, prohibited topics) as bullet points. Prevents drift and keeps outputs within policy. Contextual Anchors Provide relevant facts, examples, or style guides. Gives the model concrete reference points. Iterative Refinement Add “If unsure, ask for clarification.” Improves safety and reduces hallucinations. Quick Checklist One‑s

📖 Read the Full Tutorial

🔗 System prompts explained how to write effective system prompts 2026 — Full Guide with Code Examples

The full article includes:

✅ Step-by-step code examples (copy-paste ready)
✅ Complete working project (Spring Boot / Java)
✅ Common mistakes + fixes
✅ Production tips and benchmarks
✅ FAQ section

Published on How to Start Programming — practical AI and Java tutorials for developers.

Prompt Engineering for Code Generation: Best Practices in 2026

Rajesh Mishra — Sat, 03 Oct 2026 12:50:33 +0000

This is a summary of the full tutorial published on howtostartprogramming.in.

TL;DR: Prompt Engineering for Reliable AI Code Generation (2026) Get consistent, high‑quality code from LLMs by following these five proven tactics: Strategy Why It Works Quick Syntax 1. Structured Prompt Templates Separates intent, constraints, and examples, reducing ambiguity. {intent}\n---\n{constraints}\n---\n{examples} 2. Typed Annotations & Contracts Explicit type hints guide the model toward correct signatures and error‑free scaffolding. # @type: (int, str) → bool 3. Incremental “Chain‑of‑Thought” Coding Breaks a large task into micro‑steps, letting the model verify each piece before proceeding. Step 1: Define API\nStep 2: Write stub\nStep 3: Add tests 4. Self‑Check Assertions Embedding assertions forces the model to generate verifiable, testable code. assert isinstance(x, int), "x

📖 Read the Full Tutorial

🔗 Prompt engineering for code generation best practices 2026 — Full Guide with Code Examples

The full article includes:

✅ Step-by-step code examples (copy-paste ready)
✅ Complete working project (Spring Boot / Java)
✅ Common mistakes + fixes
✅ Production tips and benchmarks
✅ FAQ section

Published on How to Start Programming — practical AI and Java tutorials for developers.

AI document summarizer with Spring Boot and LangChain4j — Complete Guide

Rajesh Mishra — Sat, 03 Oct 2026 12:14:35 +0000

AI document summarizer with Spring Boot and LangChain4j — Complete Guide

A practical, in-depth guide to AI document summarizer with Spring Boot and LangChain4j with examples.

INTRO

Every day teams drown in PDFs, Word docs, and markdown files. Managers ask for “the gist” of a 200‑page contract, while developers need to surface key requirements from a backlog of design specs. Manually skimming each document is not only time‑consuming, it introduces human error and slows decision‑making. The real problem isn’t the volume of text—it’s the lack of a repeatable, automated way to extract concise, accurate summaries that can be trusted in a production environment.

Enter AI‑powered summarization. Large language models (LLMs) can read a document and produce a human‑like abstract in seconds. The challenge is wiring those models into a Spring Boot service that can accept uploads, stream content to an LLM, and return a clean summary—all while handling authentication, error handling, and scalability. This article teases a solution built on LangChain4j, the Java counterpart of the popular LangChain ecosystem, and shows how Spring Boot can become the glue that turns raw files into actionable insights.

WHAT YOU'LL LEARN

How to set up a Spring Boot project that integrates LangChain4j and an LLM provider (OpenAI, Anthropic, etc.).
The best way to ingest PDFs, DOCX, and plain‑text files using Apache Tika and feed them to the LLM.
Building a reusable SummarizerChain that respects token limits and supports chunking for large documents.
Securing the endpoint with Spring Security and API‑key validation for production use.
Deploying the service to a container‑friendly environment (Docker, Kubernetes) and monitoring latency.
Common pitfalls such as prompt leakage, token overrun, and handling non‑English content.

A SHORT CODE SNIPPET

@Service
public class DocumentSummarizer {

private final ChatModel chatModel = OpenAiChatModel.builder()
.apiKey(System.getenv("OPENAI_API_KEY"))
.modelName("gpt-4o-mini")
.build();

public String summarize(String content) {
// Split large text into manageable chunks
List<String> chunks = TextSplitter.recursive(1000, 200).split(content);

// Create a chain that processes each chunk and aggregates the results
SummarizerChain chain = SummarizerChain.builder()
.chatModel(chatModel)
.promptTemplate("Summarize the following text in 3 bullet points:\n\n{{input}}")
.build();

// Run the chain and join partial summaries
return chunks.stream()
.map(chain::run)
.collect(Collectors.joining("\n"));
}
}

The snippet shows the core idea: ingest raw text, split it to respect token limits, and let LangChain4j orchestrate the LLM calls. The SummarizerChain abstracts prompt handling, making the service code clean and testable.

KEY TAKEAWAYS

LangChain4j bridges the gap between Java ecosystems and modern LLM workflows, letting you reuse patterns like prompt templates, memory, and chaining without leaving Spring.
Chunking is non‑negotiable for reliable summarization; the guide explains how to choose chunk size based on model context windows.
Security and observability must be baked in from day one—API‑key guards, request tracing, and latency metrics keep the service production‑ready.
Testing LLM interactions can be deterministic by swapping the real ChatModel with a mock implementation, enabling CI pipelines to verify prompt correctness.

👉 Read the complete guide with step-by-step examples, common mistakes, and production tips:
AI document summarizer with Spring Boot and LangChain4j — Complete Guide

AI code review tools for Java and Spring Boot projects 2026 — Complete Guide

Rajesh Mishra — Fri, 02 Oct 2026 13:33:16 +0000

AI code review tools for Java and Spring Boot projects 2026 — Complete Guide

A practical, in-depth guide to AI code review tools for Java and Spring Boot projects 2026 with examples.

INTRO

Modern Java teams spend a disproportionate amount of sprint time hunting down style violations, hidden bugs, and architectural drift. Manual pull‑request reviews are valuable, but they’re also bottlenecks—especially when the codebase spans dozens of microservices built on Spring Boot. By the time a reviewer spots a subtle NPE risk or a mis‑configured bean, the change may already be merged, leaving the team to chase down regressions later.

Enter AI‑powered code review assistants. In 2026 the market has matured beyond generic linting; tools now understand Spring’s annotation‑driven wiring, can suggest idiomatic Java 21 patterns, and even flag security concerns in real time. Leveraging these assistants can shave hours off each review cycle, raise the baseline quality of every commit, and free senior engineers to focus on design discussions rather than line‑by‑line nitpicking.

WHAT YOU'LL LEARN

How the top AI reviewers (CodeGuru, DeepSource, and the new SpringSense) integrate with Maven/Gradle pipelines and GitHub Actions.
Configuring language‑specific models to recognize Spring Boot conventions such as @RestController, @Transactional, and reactive WebFlux endpoints.
Interpreting AI‑generated suggestions: distinguishing true defects from false positives and customizing rule thresholds.
Automating security checks for common pitfalls like insecure deserialization, hard‑coded credentials, and improper CORS settings.
Measuring ROI: metrics to track review time reduction, defect density, and team satisfaction after adoption.

A SHORT CODE SNIPPET

@RestController
@RequestMapping("/api/users")
public class UserController {

private final UserService service;

public UserController(UserService service) {
this.service = service;
}

@GetMapping("/{id}")
public ResponseEntity<UserDto> getUser(@PathVariable Long id) {
// AI reviewer flags: possible NullPointerException if service returns null
UserDto user = service.findById(id);
return ResponseEntity.of(Optional.ofNullable(user));
}
}

Notice how an AI reviewer can instantly point out the NPE risk and suggest wrapping the result in Optional.ofNullable.

KEY TAKEAWAYS

AI reviewers are no longer generic linters; they understand Spring Boot’s runtime semantics and can surface context‑aware issues.
Proper configuration—model selection, rule tuning, and integration points—determines whether the tool adds noise or real value.
Pairing AI suggestions with a lightweight human gate (e.g., a senior engineer’s final sign‑off) yields the best balance of speed and accuracy.
Tracking concrete metrics before and after adoption proves the investment and guides continuous improvement.

👉 Read the complete guide with step-by-step examples, common mistakes, and production tips:

AI code review tools for Java and Spring Boot projects 2026 — Complete Guide

Few-Shot Prompting vs Zero-Shot Prompting Explained (2026 Edition)

Rajesh Mishra — Thu, 01 Oct 2026 14:55:59 +0000

This is a summary of the full tutorial published on howtostartprogramming.in.

TL;DR Zero‑shot prompting asks the model to perform a task with no examples, relying solely on its pre‑training. Few‑shot prompting supplies a handful of in‑context examples (typically 1‑5) to steer the model. Zero‑shot is faster and cheaper but can be less reliable on nuanced tasks; few‑shot often boosts accuracy and robustness at a modest extra token cost. Aspect Zero‑Shot Prompting Few‑Shot Prompting Input length Only the task description (≈10‑30 tokens) Task description + 1‑5 examples (≈50‑200 tokens) Latency & cost Lowest (fewer tokens → cheaper & faster) Slightly higher (extra context tokens) Typical use‑cases Simple classification, factual Q&A, quick prototyping Complex transformations, style transfer, domain‑specific reasoning Performance gain Baseline; may suffer on ambiguous inpu

📖 Read the Full Tutorial

🔗 Few-shot prompting vs zero-shot prompting explained 2026 — Full Guide with Code Examples

The full article includes:

✅ Step-by-step code examples (copy-paste ready)
✅ Complete working project (Spring Boot / Java)
✅ Common mistakes + fixes
✅ Production tips and benchmarks
✅ FAQ section

Published on How to Start Programming — practical AI and Java tutorials for developers.

Chain of thought prompting tutorial with examples 2026 — Complete Guide 2026

Rajesh Mishra — Tue, 29 Sep 2026 14:26:31 +0000

This is a summary of the full tutorial published on howtostartprogramming.in.

Introduction Welcome to the 2026 Chain‑of‑Thought (CoT) prompting tutorial . In this post we’ll explore why CoT has become a cornerstone technique for extracting multi‑step reasoning from large language models (LLMs), how the approach has evolved over the past few years, and what best‑practice patterns you can apply today. Whether you’re a prompt engineer, a data scientist, or a developer building AI‑augmented applications, this guide will give you a concise roadmap and ready‑to‑run examples. We’ll cover: The core idea behind chain‑of‑thought prompting. Key differences between zero‑shot CoT , few‑shot CoT , and self‑consistency as of 2026. Practical syntax for popular LLM APIs (OpenAI, Anthropic, Cohere). Debugging tips and a quick checklist to ensure your prompts generate reliable reasoni

📖 Read the Full Tutorial

🔗 Chain of thought prompting tutorial with examples 2026 — Full Guide with Code Examples

The full article includes:

✅ Step-by-step code examples (copy-paste ready)
✅ Complete working project (Spring Boot / Java)
✅ Common mistakes + fixes
✅ Production tips and benchmarks
✅ FAQ section

Published on How to Start Programming — practical AI and Java tutorials for developers.

Prompt engineering complete guide for developers 2026 — Complete Guide 2026

Rajesh Mishra — Sun, 27 Sep 2026 13:23:22 +0000

This is a summary of the full tutorial published on howtostartprogramming.in.

Introduction Prompt engineering has become a cornerstone skill for developers building with large language models (LLMs) in 2026. Whether you’re integrating ChatGPT, Claude, Gemini, or a specialized domain model, the way you phrase prompts determines the quality, safety, and cost‑efficiency of the generated output. This guide provides a concise yet comprehensive overview of the prompt‑engineering workflow, the building blocks of a robust prompt, and the best practices that every developer should adopt today. Why Prompt Engineering Matters in 2026 Model maturity: Modern LLMs are more capable but also more sensitive to subtle wording. Cost control: Precise prompts reduce token usage and API spend. Safety & compliance: Structured prompts help enforce policy constraints and mitigate hallucinat

📖 Read the Full Tutorial

🔗 Prompt engineering complete guide for developers 2026 — Full Guide with Code Examples

The full article includes:

✅ Step-by-step code examples (copy-paste ready)
✅ Complete working project (Spring Boot / Java)
✅ Common mistakes + fixes
✅ Production tips and benchmarks
✅ FAQ section

Published on How to Start Programming — practical AI and Java tutorials for developers.

LLM benchmarks explained MMLU HumanEval MBPP comparison 2026 — Complete Guide 2026

Rajesh Mishra — Fri, 25 Sep 2026 13:05:48 +0000

This is a summary of the full tutorial published on howtostartprogramming.in.

Introduction Large language models (LLMs) have become the backbone of modern AI applications, but measuring their true capabilities remains a moving target. In 2026 the three most‑referenced benchmark suites are MMLU (Massive Multitask Language Understanding), HumanEval , and MBPP (Mostly Basic Python Problems). Each targets a distinct skill set—broad knowledge, code generation, and problem‑solving respectively—yet they are often cited together when ranking models. This introduction gives a quick overview of what each benchmark evaluates, why they matter, and how they differ in scope, metrics, and practical usage. Why a side‑by‑side comparison? Different axes of intelligence: MMLU tests factual and reasoning breadth across 57 subjects, while HumanEval and MBPP focus on functional correctne

📖 Read the Full Tutorial

🔗 LLM benchmarks explained MMLU HumanEval MBPP comparison 2026 — Full Guide with Code Examples

The full article includes:

✅ Step-by-step code examples (copy-paste ready)
✅ Complete working project (Spring Boot / Java)
✅ Common mistakes + fixes
✅ Production tips and benchmarks
✅ FAQ section

Published on How to Start Programming — practical AI and Java tutorials for developers.