@help_delhivery How much time you guys will take to answer me. I didn't get any help from you @delhivery. And shame on you @BenQIndia you guys stopped taking my Calls and even you didn't care to look into the matter.
@lucky_reds0@FCBarcelona Please Make sure that fans are being polite to someone. I know you you are the most fraud Club in the histroy football. But still respect the people
🚨 HIRING: Software/Systems Engineer – Intern 💻
🏢 Company: Swift
💼 Role: Software/Systems Engineer – Intern
🎓 Type: Internship
🔧 What you'll work on:
• Develop, test, maintain & enhance software for Swift Sanctions Testing
• Implement Java features and bug fixes
• Maintain automated tests
• Support CI/CD pipelines & automated deployments
• Troubleshoot software issues
🚀 If you're looking to gain real-world experience with Java, testing, CI/CD, and software systems, this could be a great opportunity!
💬 Interested? Comment “Interested” below & Follow me for more job and internship opportunities! 🔥
If you’re preparing for software engineering interviews, don’t just grind DSA.
Make sure you can explain these:
• How DNS works
• TCP vs UDP
• HTTP vs HTTPS
• REST vs gRPC
• Processes vs threads
• ACID
• Database indexing
• Caching
• Load balancing
• Message queues
• CAP theorem
• Idempotency
• Rate limiting
• Authentication vs authorization
These are the concepts that make you sound like an engineer, not just someone who solves LeetCode 🤡
Save this for future preparations 🫶
8 RAG Architectures Developers Should Know.
RAG isn't one fixed architecture. It's a spectrum of retrieval approaches that determine what gets searched, how results are ranked, and how much control the LLM has over retrieval.
Knowing these architectures is important. But understanding how to evaluate and choose the right one for your application is just as important.
Learn more about that here: https://t.co/eyIBu7mNEc
1. 𝗡𝗮𝗶𝘃𝗲 𝗥𝗔𝗚
↳ Retrieves the top matching chunks from a vector store and passes them directly to the LLM.
2. 𝗛𝘆𝗯𝗿𝗶𝗱 𝗥𝗔𝗚
↳ Combines keyword search with vector search, which helps when both exact terms and semantic meaning matter.
3. 𝗥𝗲𝗿𝗮𝗻𝗸𝗲𝗱 𝗥𝗔𝗚
↳ Retrieves a larger candidate set first, then scores and reorders chunks so the most relevant context gets used.
4. 𝗠𝘂𝗹𝘁𝗶-𝗤𝘂𝗲𝗿𝘆 𝗥𝗔𝗚
↳ Rewrites one question into multiple related queries to improve recall across different phrasings and document matches.
5. 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗥𝗔𝗚
↳ Searches broad document sections first, then narrows into smaller chunks for more precise retrieval.
6. 𝗚𝗿𝗮𝗽𝗵 𝗥𝗔𝗚
↳ Represents knowledge as entities and relationships, which helps when the answer depends on connected facts.
7. 𝗖𝗼𝗿𝗿𝗲𝗰𝘁𝗶𝘃𝗲 𝗥𝗔𝗚
↳ Checks whether retrieved context is useful and adjusts the retrieval process when the first results are weak.
8. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚
↳ Lets the LLM plan retrieval steps, choose tools or sources, and reason through multiple searches before answering.
Here's a simple mental model: Naive = fast and simple. Hybrid + Reranked = better precision. Multi-Query + Hierarchical = broader coverage. Graph = connected facts. Corrective = self-checking. Agentic = autonomous reasoning.
RAG helps models retrieve knowledge. Memory systems help them retain it. If you want to learn how production AI evolves beyond RAG into stateful memory systems, here's a great deep dive → https://t.co/MMG8YivtUW
What else would you add?
——
♻️ Repost to help others learn RAG.
🙏 Thanks to @Oracle for sponsoring this post.
➕ Follow me ( @NikkiSiapno ) to improve at AI engineering.
I created a handbook to help you learn AI agents.
It gives you:
• Must-know AI GitHub repos.
• Free courses to master AI agents.
• Papers to understand AI fundamentals.
• Curated videos to learn AI agent foundations.
• Books to get started with AI agent engineering.
• Condensed guides to broaden your AI agent knowledge.
(24 HOURS ONLY!!!)
To get it for free:
1 Follow @systemdesignone [MUST]
2 Like & Retweet to get DM
3 Reply "Handbook"
Then I'll DM you the details.
@rushilparmar57@pommy_dodo@rushilparmar57
Teri mummy certified randi, isliye tu randi o ki tarha chilla raha hai, tum bihari gandu kam dhanda hai ma chudana a jata ho. Tere mummy kal rat wala maza barama puch na. Tera Bhai ana wala hai uska khushi mana, nithala gandu bazaru aurat ki beta