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📌 𝗣𝗔𝗥𝗧 𝟮 : +5 advanced prompting techniques to increase output quality and reduce hallucinations that go beyond the usual Chain-of-Thought (CoT) What techniques work for you? __________________________________ ⚡ Hey, I'm Joanna Stoffregen from Labsbit.ai - A Gen-AI Product Development company. Reach out to learn more about building your #RAG app. #PromptEngineering #RAG #LLM

𝗥𝗔𝗚 𝗧𝗶𝗽: 6 𝗨𝘀𝗲𝗿 𝗾𝘂𝗲𝗿𝘆 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 𝘃𝘀. 6 𝗟𝗟𝗠 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 𝗳𝗼𝗿 𝗨𝘀𝗲𝗿 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 1. 𝗜𝗻𝗽𝘂𝘁 𝗦𝗮𝗻𝗶𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 To prevent injection attacks and ensure data integrity, sanitize inputs by removing or escaping special characters. Use regex patterns or specialized libraries for implementation. 2. 𝗣𝗜𝗜 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 Protect user privacy and comply with data regulations by masking or removing sensitive personal information. Implement this using named entity recognition (NER) models or pattern matching. 3. 𝗧𝗼𝘅𝗶𝗰 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 Maintain a safe environment by filtering out offensive content. Achieve this using pre-trained toxicity detection models or keyword-based filters. 4. 𝗤𝘂𝗲𝗿𝘆 𝗟𝗲𝗻𝗴𝘁𝗵 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗟𝗶𝗺𝗶𝘁𝘀 Prevent system overload and ensure efficiency by restricting overly long or complex queries. Implement character limits and parsing techniques to manage complexity. 5. 𝗥𝗮𝘁𝗲 𝗟𝗶𝗺𝗶𝘁𝗶𝗻𝗴 Ensure fair resource allocation by limiting the number of queries a user can submit in a given timeframe. Token bucket algorithms or dedicated rate-limiting services can be used for this purpose. 6. 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 Proper query processing by identifying the language of the input and routing. Use language detection libraries or models to achieve this. 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 𝗳𝗼𝗿 𝗟𝗟𝗠 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝘀 1. 𝗙𝗮𝗰𝘁𝘂𝗮𝗹 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗖𝗵𝗲𝗰𝗸𝗶𝗻𝗴 Makes sure the LLM provides correct information by comparing outputs with trusted knowledge bases. Use fact-checking APIs or cross-reference with verified sources for implementation. 2. 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 Maintain coherence across multiple interactions by tracking and enforcing consistency in LLM responses over time. Use conversation history for context checking. 3. 𝗕𝗶𝗮𝘀 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗠𝗶𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻 Prevent unfair or discriminatory responses by identifying and correcting biased language or reasoning. Implement this using bias detection models and fairness constraints. 4. 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗼𝗻 Avoid generating false information by detecting when the LLM lacks sufficient grounding. Use techniques like constrained decoding or retrieve-then-read approaches for this purpose. 5. 𝗦𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗲 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 Ensure responses are appropriate by identifying and modifying or removing sensitive content. Use content classification models and predefined policies to implement this. 6. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁 𝗘𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 Align responses with ethical guidelines and company values by applying predefined ethical constraints to generated content. ↓ → Like 👍 → Repost ♻️ 📌 P.S. Join growing 6,500+ Qendel AI community 🚀

📌 𝗣𝗔𝗥𝗧 𝟮 : +5 advanced prompting techniques to increase output quality and reduce hallucinations that go beyond the usual Chain-of-Thought (CoT) What techniques work for you? __________________________________ ⚡ Hey, I'm Joanna Stoffregen from Labsbit.ai - A Gen-AI Product Development company. Reach out to learn more about building your #RAG app. #PromptEngineering #RAG #LLM

If you are using the same text preprocessing pipeline for the LLM Embedding model and BoW/TFIDF, you're doing it wrong 𝗖𝗼𝗺𝗺𝗼𝗻 𝗺𝗶𝘀𝘁𝗮𝗸𝗲𝘀: 1/ 𝗢𝘃𝗲𝗿-𝗽𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗳𝗼𝗿 𝗟𝗟𝗠 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: Extensive preprocessing removes important context and nuances 𝗤𝘂𝗶𝗰𝗸 𝗳𝗶𝘅: Keep preprocessing minimal to preserve original text structure and meaning 2/ 𝗨𝗻𝗱𝗲𝗿-𝗽𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗳𝗼𝗿 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 (𝗲.𝗴., 𝗕𝗮𝗴 𝗼𝗳 𝗪𝗼𝗿𝗱𝘀, 𝗧𝗙-𝗜𝗗𝗙) 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: Inadequate preprocessing leads to high dimensionality and noise 𝗤𝘂𝗶𝗰𝗸 𝗳𝗶𝘅: Use more extensive preprocessing to standardize text and reduce dimensionality 3/ 𝗨𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗦𝗮𝗺𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗳𝗼𝗿 𝗕𝗼𝘁𝗵 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: LLM Embedding Models and Traditional Methods have different requirements 𝗤𝘂𝗶𝗰𝗸 𝗳𝗶𝘅: Tailor your preprocessing approach to the specific model The key difference is that LLM embedding models are designed to understand context, semantics, and nuances in language, so they benefit from receiving text that's as close to its original form as possible. 𝗧𝗿𝘆 𝘁𝗵𝗶𝘀 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗙𝗼𝗿 𝗟𝗟𝗠 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗲.𝗴., 𝗲5_𝗹𝗮𝗿𝗴𝗲_𝘃2): - Removing excessive whitespace - Handling line breaks and formatting issues - Removing or replacing special characters - Stripping HTML tags if present - Unicode normalization - Optionally handling URLs and email addresses 𝗙𝗼𝗿 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 (𝗕𝗮𝗴 𝗼𝗳 𝗪𝗼𝗿𝗱𝘀, 𝗧𝗙-𝗜𝗗𝗙): - Tokenization - Lowercasing - Stop word removal - Stemming or lemmatization - Removing punctuation and numbers - Handling of n-grams 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿: The right preprocessing can make all the difference. 𝗬𝗼𝘂𝗿 𝘁𝘂𝗿𝗻: ✍️ What's your top tip for effective text preprocessing? ↓ 📌 If you enjoyed this (and want to support us): → Like 👍 → Repost ♻️ Thanks! 📌 P.S. Join the growing 6,500+ Qendel AI community 🚀 #rag #generativeai #datascience #nlp

10 𝗺𝗼𝘀𝘁 𝘂𝘀𝗲𝗳𝘂𝗹 "𝗥𝗔𝗚", "𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴", 𝗮𝗻𝗱 "𝗔𝗴𝗲𝗻𝘁𝘀" 𝗽𝗼𝘀𝘁𝘀 𝗼𝗳 𝘁𝗵𝗲 𝘄𝗲𝗲𝗸: (𝗚𝗲𝘁 𝘁𝗵𝗲𝗺 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗺𝗶𝘀𝘀 𝘁𝗵𝗲𝗺) 👇 1/ Joanna Stoffregen Joanna is sharing 7 RAG issues in production. And solutions to sove each. Grab your link and learn below 👇 2/ Eduardo Ordax Eduardo shared advanced RAG techniques for Generative AI success! Key methods he shared include: - Basic Index Retrieval - Metadata Indexing - Reranking - Parent-Child Chunk Retrieval - and more Learn more from his recent post. ↓ 3/ Pascal Biese Getting RAG Right - All in One Go 🤖 ✔ RankRAG enhances RAG by integrating context ranking and answer generation into a single language model, reducing the need for separate, costly retriever models. Learn from his recent post ↓ 4/ Keith McNulty Keith extracted insights from large text sources! He demonstrated how to create an application that asks specific questions and summarizes opinions from respondent data. His approach is valuable for politics, customer research, and employee engagement. Check out the full repo for more details. 5/ Asif Razzaq Asif shared some interesting AI Research Updates from the last few weeks. Check them out below ↓ 6/ Kris Ograbek Kris got a HUGE gift for everyone. He has put together a looong list of AI resources for AI Engineers. Check his site below ↓ 7/ Cobus Greyling Cobus introduces us to WebVoyager. It enables building an End-to-End Web Agent with Large Multimodal Models. 8/ Pavan Belagatti Do you pick LlamaIndex or Haystack for your LLM apps? Pravan helps you choose the best for your use case. Check his post below 👇 9/ Ashutosh Hathidara Ashutosh claims great prompt can save you a finetuning job. He shared with us popular and useful prompting techniques Check his prompts below 10/ Anthony Alcaraz Anthony is looking into Multimodal RAG pipelines this time. Anthony asserts that it processes text and visuals holistically, decoding complex documents with ease. Learn more from his recent post ↓ 📚 𝗕𝗼𝗻𝘂𝘀: Qendel AI discussed how to implement “Query Rerouting” in a working way. ↓ 📌 If you enjoyed this (and want to support us): → Like 👍 → Repost ♻️ Thanks! ✍️ How do you handle query-scoping in your RAG apps? 📌 P.S. Join the growing 6,600+ Qendel AI community 🚀

5 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗰𝗵𝘂𝗻𝗸𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗹𝗲𝘃𝗲𝗹𝘀 𝗜’𝗱 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗸𝗻𝗼𝘄 (𝗶𝗳 𝗜 𝗹𝗼𝘀𝘁 𝗺𝘆 𝗺𝗲𝗺𝗼𝗿𝘆 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄) Your choice of chunking strategy isn’t just important — it’s critical. Here’s why: - It tailors documents to LLM context limits. - It shapes how LLMs interpret and use your data. - It determines what information stays or goes. The right strategy can be the difference between success and setback in your project. But don’t worry, you’ve got options. Here are 5 chunking complexities to consider: 1/ 𝗙𝗜𝗫𝗘𝗗-𝗦𝗜𝗭𝗘 𝗖𝗛𝗨𝗡𝗞𝗜𝗡𝗚 Start with the basics. Fixed-size chunking cuts text into uniform chunks: ☑ Simplifies the process ☑ Ensures consistent chunk sizes ☑ Ignores content and structure Consider tools like Langchain and llamaindex's CharacterTextSplitter or SentenceSplitter for this approach. 2/ 𝗥𝗘𝗖𝗨𝗥𝗦𝗜𝗩𝗘 𝗖𝗛𝗨𝗡𝗞𝗜𝗡𝗚 Improve structure recognition. Recursive chunking considers the text's layout: ☑ Breaks down text hierarchically ☑ Uses separators to split text iteratively ☑ Better respect document flow Langchain's RecursiveCharacterTextSplitter is a tool to explore here. 3/ 𝗗𝗢𝗖𝗨𝗠𝗘𝗡𝗧 𝗕𝗔𝗦𝗘𝗗 𝗖𝗛𝗨𝗡𝗞𝗜𝗡𝗚 Align with natural document structure. This method segments based on document layout: ☑ Tailors chunks to inherent structure ☑ Maintains logical content flow ☑ Suitable for well-structured documents This approach offers a balance between granularity and context preservation. 4/ 𝗦𝗘𝗠𝗔𝗡𝗧𝗜𝗖 𝗖𝗛𝗨𝗡𝗞𝗜𝗡𝗚 Go deeper with semantics. Semantic chunking parses based on meaning: ☑ Analyzes relationships between text segments ☑ Groups semantically similar chunks ☑ Utilizes embeddings for deeper insights Llamindex's SemanticSplitterNodeParse can help with semantic-based segmentation. 5/ 𝗔𝗚𝗘𝗡𝗧𝗜𝗖 𝗖𝗛𝗨𝗡𝗞𝗜𝗡𝗚 Let LLM decide. Agentic chunking allows the model to determine chunk boundaries: ☑ Adapts chunk size dynamically ☑ Considers context and content relevance ☑ Provides tailored chunking strategies The potential of LLM-driven chunking is huge. Want to dive deep? Learn details from the sources (with code) → Gregory Kamradt's video 👇 ----- 👉 It took me hours to curate these for you 🫵 📌 If you want to support us → Like 👍 → Repost ♻️ → Follow 🚀 Thanks! ---- ✍️ Which one has worked for you? (share with us) 📌 P.S. Follow Qendel AI to learn about prompting, RAG, Agents, and more..📌 #generativeai #prompting #rag #llm

11 𝘀𝗺𝗮𝗹𝗹 𝘁𝘄𝗲𝗮𝗸𝘀 𝘄𝗶𝘁𝗵 𝗕𝗜𝗚 𝗘𝗙𝗙𝗘𝗖𝗧 𝗼𝗻 𝗥𝗔𝗚 𝗾𝘂𝗲𝗿𝘆 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 Most of the time, the secret to huge performance changes are small tweaks. Here are 10 small tweaks you can apply today: 1. 𝗦𝗲𝗹𝗲𝗰𝘁 𝗮 𝗦𝘂𝗶𝘁𝗮𝗯𝗹𝗲 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 Supports required features and performance for your use case. Evaluate scalability, developer experience, community support, and cost when choosing. 2. 𝗛𝘆𝗯𝗿𝗶𝗱 𝗦𝗲𝗮𝗿𝗰𝗵 Combines the strengths of vector similarity and keyword matching. 3. 𝗤𝘂𝗲𝗿𝘆 𝗘𝘅𝗽𝗮𝗻𝘀𝗶𝗼𝗻 Captures related terms to find semantically similar documents. Leverage synonyms and domain-specific vocabularies for better results. 4. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 Generates query vectors that capture user intent accurately. Fine-tune a language model on domain-specific data for better relevance. 5. 𝗠𝘂𝗹𝘁𝗶-𝘀𝘁𝗮𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 Balances speed and precision with a coarse-to-fine search approach. Use a fast ANN index for the initial stage to improve efficiency. 6. 𝗤𝘂𝗲𝗿𝘆 𝗥𝗲𝗳𝗶𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 Narrows down results based on additional criteria. Allow users to filter by metadata fields for more precise searches. 7. 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 Learns from user interactions to refine search results. Utilize both explicit and implicit feedback signals for continuous improvement. 8. 𝗗𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆-𝗔𝘄𝗮𝗿𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 Ensures search results cover a broad range of relevant information. Cluster the search space and sample from each cluster to maintain diversity. 9. 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 Speeds up queries by caching results of similar searches. Implement an LRU cache with a similarity threshold to enhance performance. 10. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗿𝗮𝗽𝗵 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 Enhances search results by incorporating structured relationships between entities. Use a knowledge graph to provide context and improve the relevance of search results. 11. 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 Catches performance issues before they impact users. Set up automated relevance tests with a gold-standard dataset to monitor performance. 𝗔 𝘄𝗼𝗿𝗱 𝗼𝗳 𝗰𝗮𝘂𝘁𝗶𝗼𝗻: Even with these techniques, always approach relevance search results with a critical eye. 𝗔𝗰𝘁𝗶𝗼𝗻 𝘀𝘁𝗲𝗽: Identify one aspect of your relevance search process and pick three alternatives to test and evaluate. Your turn: ✍️ What's your favorite technique for boosting relevance search? ↓ 📌 If you enjoyed this (and want to support us): → Like 👍 → Repost ♻️ Thanks! 📌 P.S. Join the growing 6,500+ Qendel AI community 🚀 #rag #generativeai #datascience #nlp

If your RAG system doesn't answer 'key queries', (you're doing it wrong) As the wise Vilfredo Pareto once said: "80% of the results come from 20% of the efforts." 80% of user needs are covered by just 20% of question types. Yet many LLM developers struggle with this. Why? The reason is the failure to prioritize the most critical ‘Key Queries’. Here’s how you can avoid it: 1/ 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗰𝗼𝗿𝗲 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 Focus on the 20% of question types that cover 80% of user needs. Analyze user logs and support tickets or conduct surveys to determine the most common and impactful queries. 2/ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗰𝗹𝗲𝗮𝗿 𝗯𝗼𝘂𝗻𝗱𝗮𝗿𝗶𝗲𝘀 Explicitly outline what your application can and cannot answer. Set realistic expectations for users about the system's capabilities. 3/ 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗱𝗼𝗺𝗮𝗶𝗻-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 Concentrate on areas where your RAG system has unique or specialized information. Prioritize questions that leverage your proprietary data or expertise. 4/ 𝗔𝗹𝗶𝗴𝗻 𝘄𝗶𝘁𝗵 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗼𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲𝘀 Ensure the chosen question types support key business goals and user needs. Prioritize queries that drive the most value or address critical pain points.* 5/ 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝗱𝗮𝘁𝗮 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 Focus on areas where you have high-quality, comprehensive data. Avoid topics where data is sparse, outdated, or unreliable.* 🎯 𝗣𝗿𝗼 𝗧𝗶𝗽 Conduct user research and analyze query logs to identify the most important question types, then relentlessly focus on optimizing for those. 🤔 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝘆𝗼𝘂 What strategies do you use to identify and prioritize the most critical question types for your RAG-based LLM applications? ↓ 📌 If you enjoyed this (and want to support us): → Like 👍 → Repost ♻️ Thanks! ✍️ How do you handle query-scoping in your RAG apps? 📌 P.S. Join the growing 6,500+ Qendel AI community 🚀 #rag #generativeai #datascience #nlp #machinelearning

𝗥𝗔𝗚 𝗧𝗶𝗽: 6 𝗨𝘀𝗲𝗿 𝗾𝘂𝗲𝗿𝘆 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 𝘃𝘀. 6 𝗟𝗟𝗠 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 𝗳𝗼𝗿 𝗨𝘀𝗲𝗿 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 1. 𝗜𝗻𝗽𝘂𝘁 𝗦𝗮𝗻𝗶𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 To prevent injection attacks and ensure data integrity, sanitize inputs by removing or escaping special characters. Use regex patterns or specialized libraries for implementation. 2. 𝗣𝗜𝗜 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 Protect user privacy and comply with data regulations by masking or removing sensitive personal information. Implement this using named entity recognition (NER) models or pattern matching. 3. 𝗧𝗼𝘅𝗶𝗰 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 Maintain a safe environment by filtering out offensive content. Achieve this using pre-trained toxicity detection models or keyword-based filters. 4. 𝗤𝘂𝗲𝗿𝘆 𝗟𝗲𝗻𝗴𝘁𝗵 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗟𝗶𝗺𝗶𝘁𝘀 Prevent system overload and ensure efficiency by restricting overly long or complex queries. Implement character limits and parsing techniques to manage complexity. 5. 𝗥𝗮𝘁𝗲 𝗟𝗶𝗺𝗶𝘁𝗶𝗻𝗴 Ensure fair resource allocation by limiting the number of queries a user can submit in a given timeframe. Token bucket algorithms or dedicated rate-limiting services can be used for this purpose. 6. 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 Proper query processing by identifying the language of the input and routing. Use language detection libraries or models to achieve this. 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 𝗳𝗼𝗿 𝗟𝗟𝗠 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝘀 1. 𝗙𝗮𝗰𝘁𝘂𝗮𝗹 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗖𝗵𝗲𝗰𝗸𝗶𝗻𝗴 Makes sure the LLM provides correct information by comparing outputs with trusted knowledge bases. Use fact-checking APIs or cross-reference with verified sources for implementation. 2. 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 Maintain coherence across multiple interactions by tracking and enforcing consistency in LLM responses over time. Use conversation history for context checking. 3. 𝗕𝗶𝗮𝘀 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗠𝗶𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻 Prevent unfair or discriminatory responses by identifying and correcting biased language or reasoning. Implement this using bias detection models and fairness constraints. 4. 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗼𝗻 Avoid generating false information by detecting when the LLM lacks sufficient grounding. Use techniques like constrained decoding or retrieve-then-read approaches for this purpose. 5. 𝗦𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗲 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 Ensure responses are appropriate by identifying and modifying or removing sensitive content. Use content classification models and predefined policies to implement this. 6. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁 𝗘𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 Align responses with ethical guidelines and company values by applying predefined ethical constraints to generated content. ↓ → Like 👍 → Repost ♻️ 📌 P.S. Join growing 6,500+ Qendel AI community 🚀

3 𝗽𝗿𝗼𝘃𝗲𝗻 𝗥𝗔𝗚 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗦𝗘𝗖𝗥𝗘𝗧𝗦 𝘁𝗵𝗮𝘁 𝘄𝗶𝗹𝗹 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝘆𝗼𝘂𝗿 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗮 𝗳𝗮𝗻𝗰𝘆 𝗟𝗟𝗠. You think your LLM is great? It might be — but the real magic happens when you nail the retrieval. Here’s why retrieval matters the most: ✅ Retrieval pinpoints relevant data precisely. ✅ Retrieval sharpens how your LLM thinks and acts. ✅ Retrieval ensures your LLM doesn’t just sound smart—it makes it. And here's the kicker — an effective retrieval can turn your results from good to exceptional. Ready to see what takes LLMs from average to awesome? Let’s dive into the retrieval tricks that really make a difference: 1/ 𝗜𝗧𝗘𝗥𝗔𝗧𝗜𝗩𝗘 𝗥𝗘𝗧𝗥𝗜𝗘𝗩𝗔𝗟 Not just any retrieval — Iterative Retrieval is for those who require depth. Iterative retrieval: ☑ Cycles through knowledge bases repeatedly ☑ Enhances answer robustness with more context But watch out because it 𐄂 Can lead to information overload 𐄂 Might stray off-topic with too much data Perfect for tasks that need depth, but a real headache if you're not up for managing its complexity. 2/ 𝗥𝗘𝗖𝗨𝗥𝗦𝗜𝗩𝗘 𝗥𝗘𝗧𝗥𝗜𝗘𝗩𝗔𝗟 Imagine fine-tuning a camera lens to get the perfect shot — that's Recursive Retrieval. It zooms in closer with each retrieval, sharpening the focus based on previous results. Recursive retrieval: ☑ Refines queries through feedback loops ☑ Targets the most relevant information progressively ☑ Works best for ambiguous or highly specialized queries Ideal for complex and intricate queries. But if your needs are simple, this might be overkill. 3/ 𝗔𝗗𝗔𝗣𝗧𝗜𝗩𝗘 𝗥𝗘𝗧𝗥𝗜𝗘𝗩𝗔𝗟 Adaptive Retrieval is smart — maybe too smart for casual use. Think of Adaptive Retrieval as having a smart assistant who knows exactly when to fetch what you need. Adaptive retrieval: ☑ Makes retrieval decisions on the fly. ☑ Picks the best moments to seek information. ☑ Uses clever techniques like reflection tokens to decide when to search. But there’s a catch. 𐄂 It requires constant tweaking of thresholds and settings. Perfect for those who love their retrieval with a mind of their own but too complex if you prefer a set-it-and-forget-it approach. ------------- Want to learn more? → 𝗰𝗵𝗲𝗰𝗸 𝘁𝗵𝗲 𝘀𝗼𝘂𝗿𝗰𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀 👇 👉 It took me hours to curate these for you 🫵 📌 If you want to support my work (and want to make my day): → Like 👍 → Repost ♻️ → Follow 🚀 Thanks! ---- ✍️ Which one do you currently use? (share with us) 📌 P.S. Join growing 6000+ Qendel AI community 🚀 #generativeai #rag #llm