12 Hidden Claude Code Features That Change How you Work
Source:@AnalyticsVidhya analyticsvidhya.com/blog/202โฆ
#ClaudeAI #AppliedAI #GenerativeAI #LLMs #PromptEngineering #AIAgents #AIWorkflows #AIEngineering #DigitalTransformation #AppliedAI #LearningTogether #AITrends #ResponsibleAI
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๐ AI Starts With Data, Not Prompts.
Many engineers jump directly into:
โ
RAG
โ
Agents
โ
MCP
โ
LLMs
โ
Vector Databases
But there is one problem.
Most real-world enterprise data is not AI-ready.
It's buried inside:
๐ PDFs
๐ Claims
๐ Contracts
๐ Invoices
๐ Reports
๐ Scanned Documents
Before AI can reason, retrieve, or automate...
it first needs structured, high-quality data.
That's where Document Intelligence comes in.
In this free hands-on session, I walk through how to build an end-to-end Document Intelligence pipeline that transforms raw PDFs into AI-ready datasets.
Topics covered:
๐น PDF Text Extraction
๐น Claim-wise Document Processing
๐น Data Cleaning & Normalization
๐น Chunking Strategies
๐น Embeddings & Vector Databases
๐น RAG Foundations
๐น AI Agents
๐น Structured Data Extraction
๐น ML-Ready Dataset Creation
If you're a Software Engineer looking to move into AI Engineering, this is one of the most practical starting points.
AI doesn't begin with ChatGPT.
AI begins with data.
๐ฅ Full video available on my YouTube channel.
What do you think is harder today?
1๏ธโฃ Building the AI model
2๏ธโฃ Preparing the data for the AI model
#AI #AIEngineering #DocumentIntelligence #RAG #AIAgents #LLM #MachineLearning #DataEngineering #SoftwareArchitecture #CloudArchitecture #CareerGrowth #AIArchitect #RahulSahay
youtube.com/watch?v=seITz8Cxโฆ
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Submitted my Full Stack AI Engineer assignment for @getalchemyst.
12 hours of building an AI Agent Console that handled both normal and chaos modes connection drops, out-of-order messages, duplicates, latency spikes, and tool calls.
#AIEngineering #AlchemystAI
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๐ ๏ธ ๅผๆบๅทฅๅ
ทๆจ่๏ผใHivemindใโโ ็ปๆๆ AI Coding Agent ๅ
ฑไบซไธไธชๅคง่๏ผไป็ๅฎ่ฝจ่ฟน้่ชๅจๆ็ผๆ่ฝ
็จไบๅฅฝๅ ไธช AI Coding Agent ็ไบบๅคงๆฆ้ฝ้่ฟ่ฟไปถไบ๏ผๆฏไธชๅทฅๅ
ทๅญฆๅฐ็ไธ่ฅฟ้ฝ้ๅจ่ชๅทฑ็ไธไธๆ้๏ผๆขไธไธชๅทฅๅ
ทๅฐฑๅพไปๅคดๆฅ๏ผไพๅฆClaude Code ้็จ้กบๆ็ๅทฅไฝๆต๏ผๅจ Codex ๆ Cursor ้ๆ นๆฌไธ็ฅ้ใ
Hivemind ่งฃๅณ็ๆฏ่ฟไธช้ฎ้ข๏ผๅฎไฝไธบๆๆ Agent ๅ
ฑไบซ็่ฎฐๅฟๅฑ๏ผ่ชๅจๆ่ทไผ่ฏ้็ promptใๅทฅๅ
ท่ฐ็จๅๅๅบ่ฝจ่ฟน๏ผๆ้ๅคๅบ็ฐ็้ซ่ดจ้ๆจกๅผๆๆๅบๆฅ๏ผ่ฝฌๅๆๅฏๅค็จ็ SKILL.md ๆไปถ๏ผๅจๅข้ๅๅทฅๅ
ทไน้ดไผ ๆญใๆฅ่ช Activeloop๏ผY Combinator ๆฏๆ๏ผ๏ผๅผๆบๅไฝ่
็ Deep Lake ๅ้ๆฐๆฎๅบๅๅข้ใ
ๆ ธๅฟ็นๆง๏ผ
1. ่ชๅจๆจกๅผๆๆ๏ผไปไฝ ็ๅฎ้
ไฝฟ็จ่ฝจ่ฟน้ๅ็ฐไปไนๅผๅพไฟๅญ๏ผไธ้่ฆๆๅจๆด็
2. ่ทจ Agent ๆ่ฝไผ ๆญ๏ผSKILL.md ๆ ผๅผ๏ผClaude CodeใCodexใCursorใHermes Agent ็ญๅ
จๆฏๆ
3. ๆททๅๆฃ็ดข๏ผ่ฏๆณ ่ฏญไน๏ผ๏ผๅ
ณ้ฎ่ฏ็ฒพ็กฎๆฅๆพๅ่ฏญไนๆจก็ณๅน้
ๅๆจกๅผ่ฆ็
4. SQL ่ๆๆไปถ็ณป็ป๏ผ็ปๆๅๅญๅจ๏ผ่ฎฐๅฟๅฏๆฅ่ฏขใๅฏ็ๆฌๅใๅฏ่ฟ็งป
5. ไผ่ฏๆ่ฆ Wiki ่ชๅจ็ๆ๏ผ้ฟๅฏน่ฏๆต็ผฉๆๅฏ็ดขๅผ็็ฅ่ฏ็ๆฎต
6. ๅบๅ้ช่ฏ๏ผLoCoMo๏ผ๏ผๆฏๅบ็บฟไพฟๅฎ 25%ใๆฏไธช้ฎ้ขๅฐ็จ 1.7 ๅ tokenใๅๅฐ 31% ไบคไบ่ฝฎๆฌก
้กน็ฎ็นๅซ้ๅๅๆถ็จๅคไธช AI Coding Agent ๅทฅไฝใๅธๆๅทฅๅ
ทไน้ด่ฝไบ็ธใๅญฆไน ใ่ไธๆฏๅ่ชๅญค็ซ่ฟ่ก็ๅผๅ่
ๅๅทฅ็จๅข้ใ็ฎๅๅทฒ่ทๅพ 929 stars โญ๏ผๆฏๅฝๅ่ทจ Agent ๅ
ฑไบซ่ฎฐๅฟไธๆ่ฝไผ ๆญ้ขๅๆๅฎๆด็ๅผๆบๅฎ็ฐไนไธใ
ไธๅไธช Agent ็ๅ
็ฝฎ่ฎฐๅฟ็ณป็ป๏ผๅฆ Claude Code ็ CLAUDE.md๏ผ็ธๆฏ๏ผHivemind ็ๆ ธๅฟๅทฎๅผๅจไบใ่ทจๅทฅๅ
ทใ๏ผไธๆฏ Claude Code ่ชๅทฑ็่ฎฐๅฟ๏ผ่ๆฏๆๆๅทฅๅ
ทๅ
ฑไบซ็ๆ่ฝๅบ๏ผๆขๅทฅๅ
ทไธ็ญไบไปๅคดๅผๅง๏ผๅข้็ๅทฅ็จๆบๆ
งๅฏไปฅๅจๅทฅๅ
ท้ดไผ ๆญใ
github.com/activeloopai/hiveโฆ
#AIAgent #ClaudeCode #VibeCoding #codex #AIEngineering
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hardwiring your AI product to one LLM provider is a mistake.
OpenAI has an outage? everything breaks.
pricing changes? margin disappears.
a better model drops? you can't switch.
today i called 4 providers through LangChain โ same code, different models underneath.
๐ช๐๐๐ง: LangChain chat models are an abstraction layer over every major LLM. one .invoke() call works for OpenAI, Anthropic, Gemini, HuggingFace.
๐ช๐๐ฌ: provider flexibility is risk management. the model landscape moves fast. your architecture shouldn't lock you in.
๐ฅ๐๐๐ ๐ช๐ข๐ฅ๐๐: route simple queries to Gemini Flash (cheap), complex reasoning to Claude (best quality), domain tasks to a fine-tuned HuggingFace model (private). one codebase. intelligent routing.
the model is just a config. that's the shift.
code ๐
github.com/victorjanni/Langcโฆ
#LangChain #LLMs #AIEngineering #BuildingInPublic
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#๐๐ #๐
๐ซ๐๐๐๐๐๐๐๐ #๐๐ฉ๐๐ง๐๐จ๐ฎ๐ซ๐๐ #๐๐๐ #๐๐๐๐๐๐ญ๐๐ฐ๐๐ฒ #๐๐๐๐๐ฏ๐๐ฅ๐จ๐ฉ๐ฆ๐๐ง๐ญ #๐๐จ๐๐ข๐ง๐ #๐๐๐๐ก๐ข๐ง๐๐๐๐๐ซ๐ง๐ข๐ง๐ #๐๐๐ง๐๐ซ๐๐ญ๐ข๐ฏ๐๐๐ #๐๐๐ฏ๐๐ฅ๐จ๐ฉ๐๐ซ๐๐จ๐จ๐ฅ๐ฌ #๐๐๐๐ง๐ ๐ข๐ง๐๐๐ซ๐ข๐ง๐
#๐ญ๐ก๐ซ๐๐๐
#๐๐ซ๐ฒ๐ฅ๐ข๐ค๐จ๐ฏ
ALT ๐๐ก๐ ๐๐จ๐ฏ๐๐ซ ๐ข๐ฆ๐๐ ๐ ๐จ๐ ๐ญ๐ก๐ ๐ฉ๐จ๐ฌ๐ญ.
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RAG isn't a silver bullet! Large context windows hide errors. Build robust LLM apps with strong data ingress (e.g., local PDF parsing via Docling) & rigorous model evaluation. #LLMOps #AIEngineering
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๐ Day 226 โ #LearnInPublic
๐ Learned Production RAG Architectures
๐น Retrieval, reranking & generation pipelines
๐น Vector databases for semantic search
๐น Chunking, embeddings & context management
#RAG #LLM #AIEngineering #MLOps #100DaysOfCode
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The architecture evolved from a heavily modular agent system into a lean 3-agent core optimized for execution reliability and lower latency.
Demo architecture breakdown below ๐
GitHub: github.com/Hou-dini/project-โฆ
#AIEngineering #MultiAgentSystems #LLMOps #AgenticAI
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anthropic says claude wrote 80% of their production code last month. one engineer hasn't coded in 5 months.
i'm an iOS dev. i shipped an iOS Android app solo anyway.
the shift isn't coming. it's already here.
#ClaudeCode #AIEngineering
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LLMs are powerful, but they need memory & context. ๐ง
Unlocked the "Building AI Agents with @MongoDB" badge via @Credly! Ready to build autonomous agents using vector embeddings. ๐ค
Check out the badge: credly.com/badges/4bb9ea7c-8โฆ #AIEngineering #MongoDB #AIAgents #VectorSearch #mern
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๐ค You picked the best model on the market. Your app still gives inconsistent answers, forgets context mid-conversation, and breaks when real users show up.
The problem isn't the LLM. Swipe through to see what's actually going wrong - and why so many AI apps in 2026 are failing for the same reason.
Once you see it, you can't unsee it.
If you're ready to fix the layer most builders ignore, our Agentic AI Bootcamp starts July 14.
10 weeks, instructor-led, built for practitioners who are done debugging symptoms and want to fix the root cause. Link in the replies.
#ContextEngineering #AgenticAI #LLMDevelopment #AIEngineering #DataScienceDojo
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12h
Day 6 of my AI/LLM Internship Journey ๐
Today I learned:
โ
FastAPI Basics
โ
REST API Design
โ
Pydantic for Data Validation
โ
async/await in Python
โ
Virtual Environments & .env Files
โ
Built a Chat API endpoint
One thing that clicked today:
Pydantic validates incoming data before it reaches your business logic, making APIs much safer and more reliable.
Every day I'm learning something new in AI Engineering and Backend Development.
What was the most useful FastAPI concept you learned as a beginner?
#AI #LLM #MachineLearning #FastAPI #Python #GenerativeAI #AIEngineering
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Deleting for one user never breaks another user's search โ Concurrent uploads are handled safely with no extra infra Vectors are shared. Access is isolated. That's the right model for multi-user RAG.
#RAG #Qdrant #VectorDatabase #LLM #AIEngineering #Python
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I find it funny everyone talking about local models coz fable was banned. Local models are far from anything fable, opus, gpt 5.5 or even deepseek, kimi, glm and others can achieve. Letโs stop kidding ourselves and be realistic
#aiengineering
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The best AI engineers don't chase every new model.
They focus on: โข Understanding the problem โข Evaluating outputs โข Building reliable systems โข Delivering value
Tools change. Fundamentals last.
#AIEngineering #MachineLearning
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Today I am introducing and continuing the Gen AI with LangChain series with a focus on an important topic called RAG (Retrieval-Augmented Generation), a highly useful generative AI application widely adopted after the advent of large language models (LLMs).
Earlier where four main components essential to RAG were covered extensively:
-Document Loaders: Tools for loading data from various sources.
-Text Splitters: Mechanisms to divide large texts into manageable chunks.
-Vector Stores: Databases that store text as embeddings (dense vector representations).
-Retrievers: Components for semantic search over vector stores to fetch relevant chunks.
With this foundation, the time is now right to understand RAG in detail, including its definition, necessity, and functioning.
It begins with the โWhyโ of RAG by discussing limitations of LLMs:
-LLMs are large transformer-based neural networks with billions of parameters (weights and biases).
-They undergo pre-training on massive datasets (internet-scale) that encodes parametric knowledge inside the modelโs parameters.
-The greater the parameter count (e.g.,7Bโ13Bโ70B)(e.g.,7Bโ13Bโ70B), the more knowledge the model can potentially store.
-Users query LLMs via prompts to access this stored knowledge.
However, there exist three major limitations or problem scenarios in the traditional prompt-LLM approach:
1. Private Data Queries:
The LLM cannot answer questions about private or domain-specific data not seen during pre-training. For example, querying about a course videoโs content on a private website fails because it was never part of the pre-training data.
2. Recent Data/Timeliness:
LLMs have a fixed knowledge cutoff date. Text or events after that point (new news, updates) won't be reflected in their parametric knowledge, leading to failures in answering up-to-date questions.
3. Hallucination:
The model may generate factually incorrect information confidently. For instance, falsely claiming Einstein played football professionally. This issue arises due to the probabilistic nature of LLMs trying to predict likely responses, sometimes inventing facts.
These three challenges make relying solely on parametric knowledge insufficient.
Fine-tuning is introduced as one common technique to partially address these problems:
-Fine-tuning means retraining a pre-trained model on a smaller, domain-specific dataset.
-This adapts the model to additional knowledge (e.g., medical domain), improving accuracy on specialized queries.
-Two methods of fine-tuning are explained:
1. Supervised Fine-tuning: Using labeled prompt-response pairs (inputs and desired outputs) - usually thousands to millions of examples.
2. Continued Pre-training (Unsupervised): Training on domain-specific raw texts without labels.
-Additional advanced methods like RLHF (Reinforcement Learning with Human Feedback) and LoRA (parameter-efficient tuning) are mentioned.
-An analogy is drawn: Fine-tuning is like an engineering graduate undergoing company-specific on-the-job training to perform domain-specific tasks well.
How fine-tuning solves the three problems:
-Private Data: The modelโs parametric knowledge now includes private/domain-specific info after tuning.
-Recent Data: Frequent re-tuning with updated datasets reflects new information. However, this gets expensive and repetitive if updates are frequent.
-Hallucination: Fine-tuning on tricky prompts with explicit "I don't know" answers can reduce hallucination by teaching the model to avoid guessing.
Limitations of fine-tuning:
-Computationally expensive to train huge models.
Requires expert AI engineers and data scientists.
Frequent updating is costly and slow, making it impractical for rapidly changing data.
Hence, fine-tuning, while helpful, is not always the ideal solution.
A second, alternative technique called In-Context Learning is introduced:
-Defined as the ability of large language models (like GPT-5, Claude, LLaMA) to learn how to perform a task by observing examples in the prompt itself without updating the model weights.
-This is also known as few-shot prompting, where examples of input-output pairs are provided inline, and the model generalizes from these to new inputs.
The idea is extended: Instead of few-shot prompting with examples of task solutions, send the task context itself as prompt context.
-Example: A long lecture on linear regression (2 hours).
-When a student asks a question about "gradient descent," embed the specific transcript segment related to gradient descent as context in the prompt.
-This approach provides the model with external knowledge during prompt time rather than relying solely on its parametric knowledge.
This technique is defined as Retrieval-Augmented Generation (RAG):
-RAG means making a language model smarter by injecting external relevant information (context) at query time.
-The prompt comprises both the user query and surrounding context retrieved from external knowledge.
-The LLM uses both its internal parametric knowledge and the provided external context to answer, drastically improving accuracy and reliability.
Technical architecture of a RAG system broadly includes two core concepts:
-Information Retrieval: A well-studied traditional area focused on efficiently finding relevant documents or data segments.
-Text Generation: Enabled by LLMs for generating natural language answers.
RAG combines these:
The process can be divided into four steps:
1.Indexing
Preparing an external knowledge base
Loading and processing source documents
2.Retrieval
Searching the knowledge base
Finding relevant pieces (chunks) based on the user query
3.Augmentation
Creating a prompt that combines the user query and retrieved context
Guiding the LLM with relevant information
4.Generation
The LLM generates the answer
Uses the prompt and its internal knowledge to respond
Overall, RAG is positioned as a better, cheaper, and simpler alternative to fine-tuning that does not require retraining or labeled data, just maintaining an updated vector store.
Next , I will talk about "Tools".
#GenerativeAI #LangChain #AIEngineering #LLM #RAG
Jun 10
Today, I continue the Gen Ai/LangChain series focusing on retrievers, a crucial component in building Retrieval-Augmented Generation (RAG) applications.
Retrievers are central in RAG systems to fetch relevant documents based on user queries.
I will explain what retrievers are, their need, different types, and provide live code demonstrations.
This is the fourth core component after covering Document Loader, Text Splitter, and Vector Stores, preparing viewers to start working with RAG systems.
What Are Retrievers?
-A retriever is a component in LangChain that fetches relevant documents from a data source in response to a user query.
-The data source can be a vector store, API, or any repository where documents are stored.
-Process: The user inputs a query โ the retriever searches the data source โ it identifies and retrieves the most relevant documents โ returns them as LangChain Document objects.
-Functionally, a retriever acts like a search engine.
-Retrievers are runnable objects in LangChain with an invoke() method, allowing easy chaining and integration. Multiple retriever types exist to accommodate different use cases.
Classification of Retriever Types
1. Data Source Type :
The nature/source of documents the retriever queries.
Examples:
Wikipedia Retriever
Vector Store Retriever
Archive Retriever (research papers)
2. Search Strategy :
The algorithmic method used to find relevant documents within the data source.
Examples:
Maximum Marginal Relevance (MMR)
Multi-Query Retriever
Contextual Compression Retriever
-Many retrievers exist in LangChain (20-30 ), covering diverse scenarios.
- I will focus on the most relevant and widely-used retrievers with references for further reading.
Wikipedia Retriever
-Queries the Wikipedia API using user queries to fetch relevant articles as LangChain Document objects.
-Works via keyword matching (not semantic or syntactic search).
-Acts as a search engine over Wikipedia content, not as a document loader that pulls all articles.
-Example: Query about "Geopolitical history of India and Pakistan from a Chinese perspective" โ Wikipedia API returns relevant articles based on keyword overlap.
Vector Store Retriever
--The most common retriever type in LangChain.
Performs semantic similarity search by comparing vector embeddings of the user query with vectors of stored documents.
-Workflow:Documents are embedded into dense vectors using an embedding model.
-Vectors are stored in a vector database (e.g., Chroma, Faiss).
-Query is vectorized and compared against stored vectors to identify most similar documents.
-Supports integration with multiple vector stores and embedding models.
Why use a vector store retriever instead of direct similarity search?
-Vector stores provide only one search strategy (similarity-based).
-Retrievers offer abstraction and the ability to implement multiple search strategies beyond simple similarity search, enhancing flexibility and advanced querying capabilities.
Search Strategy-Based Retrievers: Maximum Marginal Relevance (MMR)
-Problem addressed: Simple similarity search often returns documents that are redundantโmultiple docs expressing the same idea, limiting diversity in results.
-MMR balances relevance and diversity by selecting documents that are both relevant to the query and dissimilar to already selected documents.
Additional Retriever Types and Resources
-Beyond the featured retrievers, many others exist such as:
Parent Document Retriever
Time Weighted Vector Retriever
Self Query Retriever
On-sample Retriever
Multi Retriever
-For comprehensive detail and code on these retrievers, LangChain documentation is recommended (linked in video description).
-The landscape is broad, and covering all retrievers in one video is impractical.
-Future videos/projects may cover advanced retriever implementations as needed.
Why So Many Retriever Types?
-Different retrievers solve distinct problems and optimize retrieval for various contexts.
-From a generative AI perspective, retrievers are typically used in RAG-based systems:Simple RAG systems may return suboptimal or redundant retrievals.
-Advanced retrievers improve retrieval quality, relevance, and diversity, enhancing downstream generation quality.
-Developers often experiment by replacing or upgrading retrievers in an existing RAG system to improve performance.
-Understanding retrievers is essential for building robust and efficient RAG applications with LangChain.
Key Insights
-Retrievers are fundamental building blocks of RAG systems for effective document retrieval.
-Multiple retrieval strategies exist to tackle inherent challenges like ambiguity, redundancy, and document size.
-LangChainโs design treats retrievers as runnable, extensible components supporting flexible integration.
-Selection and tuning of retriever types directly impact the quality and diversity of results, which in turn influences the final generative AI outputs.
-Understanding these retriever types enables developers to build advanced, optimized RAG workflows adapted to specific application contexts.
#GenerativeAI #LangChain #AIEngineering #LLM #RAG
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dashboards are for humans. logs are for devs. but what about your agents? iโm moving toward structured cli and api outputs designed for agentic consumption. if the agent canโt read the result, the task isn't done.
building for agents means moving past visual feedback. i prefer schema-first architecture where the output is designed to be parsed, not just viewed. when your consumer is an agent, structure is the priority.
#laravel #aiengineering #api #webdev
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AI is already the future. Are you building it?
SVIT Anantapur โ B.Tech & M.Tech in AI & ML. NAAC Accredited. Admissions Open 2026โ27. svitatp.ac.in
#SVITAnantapur #AIEngineering
ALT SVIT Anantapur Engineering College poster showing B.Tech CSE AI ML and M.Tech AI Machine Learning admissions open 2026โ27. NAAC Accredited, AICTE Approved, JNTUA Affiliated. 100% Placement Assistance. Contact 7093227022.
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