AI Language Processing

Most people think of RAG (Retrieval-Augmented Generation) as: 𝘘𝘶𝘦𝘳𝘺 → 𝘝𝘦𝘤𝘵𝘰𝘳 𝘋𝘉 → 𝘓𝘓𝘔 → 𝘈𝘯𝘴𝘸𝘦𝘳 But that’s just step one. In 2025, we’re seeing a shift toward 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 systems—where LLMs don’t just retrieve and respond, but also 𝗿𝗲𝗮𝘀𝗼𝗻, 𝗽𝗹𝗮𝗻, 𝗮𝗻𝗱 𝗮𝗰𝘁. This visual breakdown captures the core idea: → A query is embedded and used to fetch relevant chunks from a vector DB. → An 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 leverages those chunks to craft context-aware prompts. → It can also invoke external tools:     • Web Search    • APIs    • Internal Databases This unlocks workflows that are: • Dynamic • Context-aware • Action-oriented    It's not just answering — it's deciding 𝘄𝗵𝗮𝘁 𝘁𝗼 𝗱𝗼 𝗻𝗲𝘅𝘁. Toolkits like 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, 𝗖𝗿𝗲𝘄𝗔𝗜, 𝗚𝗼𝗼𝗴𝗹𝗲 𝗔𝗗𝗞, and 𝗔𝘂𝘁𝗼𝗚𝗲𝗻 are making this architecture practical for real-world systems. What tools or techniques are 𝘺𝘰𝘶 using to take your LLM apps beyond static chatbots?

After LLMs, LCMs: Large Concept Models. Because we don't think in tokens, so why should machines? Meta's AI Research team have built a model that operates at sentence level, resulting in some substantial performance improvements, notably in zero-shot translation tasks and text continuation. This is a promising direction, with great scope to vary the unit of 'concept', which I expect will work better at sub-sentence level. It is particularly interesting to envisage how this could be applied in Humans + AI cognition, with better integration with human thinking by working at more similar semantic levels. Key insights in the paper (link to paper and GitHub repo in comments): 🌟 Revolutionizing Semantic Understanding with Concepts. The LCM architecture shifts focus from token-level processing to higher-level "concepts," such as sentences. This abstraction enables reasoning across 200 languages and multiple modalities, surpassing conventional token-based LLMs. Practically, this design promotes efficiency in multilingual tasks, enabling scalable applications in text and speech analysis. 📚 Explicit Hierarchical Structuring for Enhanced Coherence. By processing information in a structured flow—from abstract concepts to detailed content—the LCM mirrors human planning methods like outlining essays or talks. This hierarchical design supports better readability and interactive edits, making it ideal for generating and analyzing long-form content. 🧠 Zero-Shot Generalization Across Languages and Modalities. Thanks to its use of the SONAR embedding space, the LCM excels in zero-shot tasks across text, speech, and experimental American Sign Language inputs. This capability reduces dependency on fine-tuning for new languages or modalities, broadening its use in global communication tools. 🔀 Diffusion-Based Models Offer Robust Text Generation. Diffusion-based methods within LCM demonstrate superior performance in generating coherent, semantically rich continuations for texts compared to other approaches like simple regression or quantization. These models also provide a balance between accuracy and creative variability. 🚀 Efficient Handling of Long Contexts. The LCM's concept-based representation significantly reduces the sequence length compared to token-based models. This efficiency allows it to process lengthy documents with reduced computational overhead, enhancing feasibility for large-scale applications. 🤖 Opportunities in Modality Integration. With modular encoders and decoders, the LCM avoids the competition issues faced by multimodal models. This extensibility supports the independent development of language or modality-specific components, making it a versatile backbone for diverse AI systems.

UC Berkeley and UCSF just brought real-time speech back to someone who couldn’t speak for 18 years (insane!). For people with paralysis and anarthria, the delay and effort of current AAC tools can make natural conversation nearly impossible. 𝗧𝗵𝗶𝘀 𝗻𝗲𝘄 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗻𝗲𝘂𝗿𝗼𝗽𝗿𝗼𝘀𝘁𝗵𝗲𝘀𝗶𝘀 𝘀𝘁𝗿𝗲𝗮𝗺𝘀 𝗳𝗹𝘂𝗲𝗻𝘁, 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝘀𝗽𝗲𝗲𝗰𝗵 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗳𝗿𝗼𝗺 𝗯𝗿𝗮𝗶𝗻 𝘀𝗶𝗴𝗻𝗮𝗹𝘀 𝗶𝗻 𝗿𝗲𝗮𝗹 𝘁𝗶𝗺𝗲 𝘄𝗶𝘁𝗵 𝗻𝗼 𝘃𝗼𝗰𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗱. 1. Restored speech in a participant using 253-channel ECoG, 18 years after brainstem stroke and complete speech loss. 2. Trained deep learning decoders to synthesize audio and text every 80 ms based on silent speech attempts, with no vocal sound needed. 3. Streamed speech at 47.5 words per minute with just 1.12s latency = 8× faster than prior state-of-the-art neuroprostheses. 4. Matched the participant’s original voice using a pre-injury recording, bringing back not just words but vocal identity. Bimodal decoder architecture they used was cool. It's interesting how they got to low-latency and a synchronized output from the system. This was done by sharing a neural encoder and employing separate joiners and language models for both acoustic-speech units and text Other tidbits used was convolutional layers with unidirectional GRUs and LSTM-based language models. Absolutely love seeing AI used in practical ways to bring back joy and hope to people who are paralyzed!! Here's the awesome work: https://lnkd.in/ghqX5EB2 Congrats to Kaylo Littlejohn, Cheol Jun Cho, Jessie Liu, Edward Chang, Gopala Krishna Anumanchipalli, and co! I post my takes on the latest developments in health AI – 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! Also, check out my health AI blog here: https://lnkd.in/g3nrQFxW

🚨 Singaporean Minister: "LLMs trained on Western, English-centric data struggle in Southeast Asia." Many don't know, but Singapore has developed its own open-source LLMs. Here's how multilingualism is fueling AI nationalism: According to its developers, the Southeast Asian Languages in One Network (SEA-LION) is a family of open-source LLMs that better capture Southeast Asia’s peculiarities, including languages and cultures. According to its developers, the Singapore-based models: "understand nuances in Southeast Asian languages and demonstrate greater awareness of cultural context specific to the region. This lowers the bar for adoption by governments, enterprises, and academia, while effectively expanding the Southeast Asian languages and cultural representation in the mainstream LLMs which are currently dominated by models predominantly trained on a corpus of English data from the western, developed world." Multilingualism is emerging as an important source of local and national AI development in various parts of the world. If you remember my recent post about the Swiss LLM, this was one of the focuses of their national model. In the case of SEA-LION, it's trained on more content produced in Southeast Asian languages, such as Thai, Vietnamese, and Bahasa Indonesia. Different from other technologies, LLMs (large LANGUAGE models) are fully dependent on the nuances, biases, and quality of the dataset, including from a linguistic perspective. Western, English-based models will not account for the subtleties and nuances of other languages. And language is an integral and essential part of culture. Especially now that LLMs are being integrated everywhere, countries are beginning to reject LLMs that don't take their language and culture into account. It's interesting to observe that Singapore wants to expressly distance itself from American and Chinese models (this has not been the case in the UK, for example, which has recently signed an agreement with OpenAI, an American company). - I've been writing about the emergence of a new AI nationalism in my newsletter (I'm adding a link to a recent essay below), and there are already interesting examples coming from Switzerland, Germany, China, the UK, and Singapore. This is a growing AI governance trend with political and economic ramifications. I'll keep you posted! - 👉 NEVER MISS my analyses and curations on AI: join my newsletter's 69,800+ subscribers (link below)

A few days ago, we began our year-end series highlighting the top 5 news stories our readers found the most interesting. 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐧𝐮𝐦𝐛𝐞𝐫 𝟏:  𝐒𝐩𝐞𝐚𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐯𝐨𝐜𝐚𝐥 𝐜𝐨𝐫𝐝𝐬, 𝐭𝐡𝐚𝐧𝐤𝐬 𝐭𝐨 𝐚 𝐧𝐞𝐰 𝐀𝐈-𝐚𝐬𝐬𝐢𝐬𝐭𝐞𝐝 𝐰𝐞𝐚𝐫𝐚𝐛𝐥𝐞 𝐝𝐞𝐯𝐢𝐜𝐞 Bioengineers invented a thin (weighing only 7 grams), flexible device that adheres to the neck and translates the muscle movements of the larynx with the assistance of machine-learning technology - with nearly 95% accuracy - into audible speech! What an amazing technological support for people who have lost the ability to speak due to vocal cord problems. "The tiny new patch-like device is made up of two components. One, a self-powered sensing component, detects and converts signals generated by muscle movements into high-fidelity, analyzable electrical signals; these electrical signals are then translated into speech signals using a machine-learning algorithm. The other, an actuation component, turns those speech signals into the desired voice expression." Next: clinical trials, as well as enlarging the vocabulary of the device through machine learning.

Prompt engineering is not dead, but it’s no longer enough. Context Engineering is what is filling the gaps 👇 If you’ve ever built a serious LLM workflow, you already know: most failures don’t come from bad prompts, they come from missing, outdated, or misformatted context. That’s why I’ve shifted from obsessing over the perfect "prompt", to designing robust context systems around the model. Andrej Karpathy famously tweeted- "+1 to context engineering, over prompt engineering" Let’s talk about Context Engineering. 🔍 What is it? Context engineering = building dynamic systems that deliver the right info, in the right format, at the right time so the model can actually accomplish the task. It’s more than just phrasing. It’s about memory, retrieval, structured outputs, history, and tool awareness. Context engineering is what allows the model to act coherently across long conversations, use retrieved knowledge intelligently, and collaborate with other agents/tools. 🧱 Four Pillars of Context Engineering: → Write: Persist info that lives outside the context window ✦ Scratchpads, logs, long-term memory → Select: Pull in only relevant context ✦ Redis retrieval, memory recall, tool specs → Compress: Summarize to save tokens ✦ Auto-trimming (like Claude), compact context windows → Isolate: Prevent interference between contexts ✦ Multi-agent sub-contexts, sandboxed processes 🔄 Lifecycle of Context Engineering: Acquire → Process → Store → Retrieve → Update It’s an entire system. Not a one-shot prompt. 🧠 Prompt vs. Context Engineering: ✦ Prompt Engineering = phrasing the instruction ✦ Context Engineering = building the system around the model (memory, retrieval, tools, workflows) If you’re building agents, multi-turn apps, or systems with state, context engineering is non-negotiable. ⚙️ Some tools I use: → LangGraph / LangSmith for tracing + long-term state → LlamaIndex, vector DBs for retrieval → Orchestration libs for multi-agent workflows 🚫 Common pitfalls to avoid: → Overengineering too early → Garbage in, garbage out (poor validation) → Token bloat, context drift, or stale data → Ignoring security/privacy of memory layers The future isn’t just about prompt engineering. It’s about designing intelligent context layers around your models. Save this for your next AI system design session. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

LoRA Learns Less and Forgets Less: When I saw a new, comprehensive empirical study of Low-Rank Adaptation for finetuning LLMs, I had to read it! Here are the main takeaways. This study aimed to compare LoRA to full finetuning on two different target domains: programming and mathematics (rather than the usual general instruction following tasks.) Moreover, the authors also compared instruction finetuning and continued pretraining scenarios. LoRA vs full finetuning? It's maybe as expected: It all comes down to a learning-forgetting trade-off. Full finetuning results in stronger performance on the new target domain, whereas LoRA maintains better performance on the original source domain. Intuitively, I suspect this is simply a side effect from LoRA changing fewer parameters in the model -- the goal of LoRA (as its name implies) is a low-rank adaptation, that is, not substantially modifying all model parameters. Nonetheless, it's really nice to see this all laid out and analyzed in great experimental detail. (The experiments were done with 7B and 13B Llama 2 models). In practice, it's also often not a question whether to use full finetuning or LoRA as the latter may be the only feasible one due to its memory savings and lower storage footprint. By the way, I am only showing a small fraction of the results. The paper has lots of additional plots and experiments that I recommend checking out. For example, they show that - applying LoRA to all layers results in a bigger improvement than increasing the rank; - LoRA has a stronger regularizing effect (=ability to reduce overfitting) than dropout and weight decay So, as a key practical insight, if your model overfits too much on the new target domain, it may be worthwhile switching to LoRA. Something that I observed in my recent GPT classification finetuning experiments as well. Link to the paper: LoRA Learns Less and Forgets Less, https://lnkd.in/gBNZ8Aaz

Prompting tells AI what to do. But Context Engineering tells it what to think about. Therefore, AI systems can interpret, retain, and apply relevant information dynamically, leading to more accurate and personalized outputs. You’ve probably started hearing this term floating around a lot lately, but haven’t had the time to look deep into it. This quick guide can help shed some light. 🔸What Is Context Engineering? It’s the art of structuring everything an AI needs not just prompts, but memory, tools, system instructions, and more to generate intelligent responses across sessions. 🔸How It Works You give input, and the system layers on context like past interactions, metadata, and external tools before packaging it into a single prompt. The result? Smarter, more useful outputs. 🔸Key Components From system instructions and session memory to RAG pipelines and long-term memory, context engineering pulls in all these parts to guide LLM behavior more precisely. 🔸Why It’s Better Than Prompting Alone Prompt engineering is just about crafting the right words. Context engineering is about building the full ecosystem, including memory, tool use, reasoning, reusability, and seamless UX. 🔸Tools Making It Possible LangChain, LlamaIndex, and CrewAI handle multi-step reasoning. Vector DBs and MCP enable structured data flow. ReAct and Function Calling APIs activate tools inside context. 🔸Why It Matters Now Context engineering is what makes AI agents reliable, adaptive, and capable of deep reasoning. It’s the next leap after prompts, welcome to the intelligence revolution. 🔹🔹Structuring and managing context effectively through memory, retrieval, and system instructions allows AI agents to perform complex, multi-turn tasks with coherence and continuity. Hope this helps clarify a few things on your end. Feel free to share, and follow for more deep dives into RAG, agent frameworks, and AI workflows. #genai #aiagents #artificialintelligence

𝗧𝗵𝗶𝘀 𝗶𝘀 𝗵𝗮𝗻𝗱𝘀-𝗱𝗼𝘄𝗻 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗰𝗹𝗲𝗮𝗻𝗲𝘀𝘁 𝘃𝗶𝘀𝘂𝗮𝗹 𝗲𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 𝗜’𝘃𝗲 𝘀𝗲𝗲𝗻! ⬇️ This 60-second clip explains a concept so fundamental, it powers almost everything in GenAI today: 𝗪𝗼𝗿𝗱 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 𝗟𝗲𝘁'𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻: ⬇️ ➜ AI doesn’t “read” words like we do. Every input — whether it’s a sentence, a word, or a name — is first broken down into smaller pieces called tokens. ➜ Each of these tokens is then mapped to a set of numbers. This numeric representation is called an embedding. ➜ You can think of an embedding as a position in a high-dimensional space — not just a point on a 2D map, but a vector in a space with hundreds of dimensions. In that space, similar meanings end up closer together. ➜ That’s how analogies become math: If the vector for “Hitler” is near “Germany”, and “Mussolini” is close to “Italy”, the model can compute: Hitler + Italy – Germany ≈ Mussolini It’s not because the model knows history — it’s because the geometry of these word vectors reflects the structure of language, culture, and context learned from billions of examples. This is what enables AI to reason with language — not just store it. → “Hitler + Italy – Germany = Mussolini” → “King – Man + Woman = Queen” → “Paris – France + Italy = Rome” 𝗧𝗵𝗶𝘀 𝗶𝘀𝗻’𝘁 𝗺𝗮𝗴𝗶𝗰. 𝗜𝘁’𝘀 𝗺𝗮𝘁𝗵. 𝗔𝗻𝗱 𝗶𝘁’𝘀 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝗵𝗼𝘄 𝗟𝗟𝗠𝘀 𝗹𝗶𝗸𝗲 𝗚𝗣𝗧-4, 𝗖𝗹𝗮𝘂𝗱𝗲, 𝗚𝗲𝗺𝗶𝗻𝗶 𝗼𝗿 𝗠𝗶𝘀𝘁𝗿𝗮𝗹 𝗹𝗲𝗮𝗿𝗻 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀, 𝗮𝗻𝗮𝗹𝗼𝗴𝗶𝗲𝘀, 𝗮𝗻𝗱 𝗻𝘂𝗮𝗻𝗰𝗲 — 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲. 𝗜𝘁’𝘀 𝘁𝗵𝗲 𝗰𝗼𝗿𝗲 𝗶𝗱𝗲𝗮 𝘁𝗵𝗮𝘁 𝗺𝗮𝗱𝗲 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝘄𝗼𝗿𝗸. 𝗔𝗻𝗱 𝗶𝘁’𝘀 𝘁𝗵𝗲 𝗿𝗲𝗮𝘀𝗼𝗻 𝗔𝗜 𝘁𝗼𝗱𝗮𝘆 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗰𝗼𝗽𝘆 𝘁𝗲𝘅𝘁 — 𝗶𝘁 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝘀 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. This snippet is part of 3Blue1Brown video on transformers: https://lnkd.in/dM7B9FMY (I highly recommend to watch the full video and follow their channel!)

𝐅𝐫𝐨𝐦 𝐏𝐫𝐨𝐭𝐨𝐭𝐲𝐩𝐞 𝐭𝐨 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧: 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐢𝐧𝐠 𝐋𝐋𝐌-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 It’s one thing to build a cool LLM demo. It’s another to make it scalable, safe, and production-grade. Whether you’re building a chatbot, assistant, or workflow engine, the architecture around the model is what determines usability, reliability, and impact. 4 𝐂𝐨𝐦𝐦𝐨𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐢𝐧 𝐋𝐋𝐌 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: 𝐏𝐫𝐨𝐦𝐩𝐭-𝐁𝐚𝐬𝐞𝐝 𝐀𝐩𝐩𝐬 𝘋𝘪𝘳𝘦𝘤𝘵 𝘱𝘳𝘰𝘮𝘱𝘵 → 𝘳𝘦𝘴𝘱𝘰𝘯𝘴𝘦 ✅ Fast to build ❌ Hard to scale or govern 𝐑𝐀𝐆 (𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧) 𝘍𝘦𝘵𝘤𝘩𝘦𝘴 𝘳𝘦𝘭𝘦𝘷𝘢𝘯𝘵 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘪𝘯 𝘳𝘦𝘢𝘭 𝘵𝘪𝘮𝘦 ✅ Boosts factual accuracy ❌ Needs good retrieval, chunking, and indexing logic 𝐀𝐠𝐞𝐧𝐭-𝐁𝐚𝐬𝐞𝐝 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝘈𝘨𝘦𝘯𝘵𝘴 𝘳𝘦𝘢𝘴𝘰𝘯, 𝘱𝘭𝘢𝘯, 𝘢𝘯𝘥 𝘢𝘤𝘵 ✅ Great for dynamic, tool-using tasks ❌ Requires orchestration and safe execution strategies 𝐋𝐋𝐌 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝘊𝘩𝘢𝘪𝘯𝘴 𝘮𝘶𝘭𝘵𝘪𝘱𝘭𝘦 𝘓𝘓𝘔 𝘤𝘢𝘭𝘭𝘴 (𝘦.𝘨., 𝘦𝘹𝘵𝘳𝘢𝘤𝘵 → 𝘢𝘯𝘢𝘭𝘺𝘻𝘦 → 𝘴𝘶𝘮𝘮𝘢𝘳𝘪𝘻𝘦) ✅ Modular and testable ❌ Adds latency and system complexity 𝐊𝐞𝐲 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐚𝐥 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: 𝐇𝐨𝐬𝐭𝐞𝐝 𝐯𝐬. 𝐎𝐩𝐞𝐧-𝐒𝐨𝐮𝐫𝐜𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (e.g., GPT vs. Mistral) 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬: LangChain, LlamaIndex, Semantic Kernel 𝐌𝐞𝐦𝐨𝐫𝐲 & 𝐒𝐭𝐚𝐭𝐞: Chat history, user profile, external context 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Logging, feedback loops, versioning 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 & 𝐒𝐚𝐟𝐞𝐭𝐲: Guardrails, validation, fallback paths 𝐋𝐨𝐨𝐤𝐢𝐧𝐠 𝐀𝐡𝐞𝐚𝐝 New standards like 𝐀𝐧𝐭𝐡𝐫𝐨𝐩𝐢𝐜’𝐬 𝐌𝐨𝐝𝐞𝐥 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥 (𝐌𝐂𝐏) and 𝐆𝐨𝐨𝐠𝐥𝐞'𝐬 𝐀𝐠𝐞𝐧𝐭-𝐭𝐨-𝐀𝐠𝐞𝐧𝐭 (𝐀2𝐀) 𝐩𝐫𝐨𝐭𝐨𝐜𝐨𝐥 are early steps toward more interoperable, modular AI ecosystems. If adopted widely, they could enable agents and models to share context and collaborate more effectively — powering next-gen enterprise workflows. 𝐔𝐩 𝐧𝐞𝐱𝐭: How to design guardrails and safety layers to ensure your LLM applications are reliable, responsible, and ready for production. Which of these patterns are you exploring in your stack? #engineeringtidbits #LLMs #RAG #AIArchitecture #Agents #MCP #A2A #LangChain #EnterpriseAI #NLP