Integrating 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 Systems via 𝗠𝗖𝗣 👇 If you are building RAG systems and packing many data sources for retrieval, most likely there is some agency present at least at the data sour…


LinkedIn Content Strategy & Writing Style
Founder @ SwirlAI • Ex-CPO @ neptune.ai (Acquired by OpenAI) • UpSkilling the Next Generation of AI Talent • Author of SwirlAI Newsletter • Public Speaker
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Aurimas Griciūnas positions himself as a high-level practitioner-educator who bridges the gap between theoretical AI research and production-grade AI engineering. His content strategy centers on the "how-to" of the post-prototype era, focusing on recurring technical themes like Agentic RAG, Model Context Protocol (MCP), and evaluation-driven development. He is notable for his ability to translate complex architectural patterns into standardized mental models for developers, moving beyond simple chatbot tutorials to address the systemic challenges of latency, reliability, and token cost. By intersecting his deep product background from the Neptune.ai acquisition with hands-on technical instruction, he provides a unique value proposition of operationalizing AI at scale while building a community around the next generation of AI talent.
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Integrating 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 Systems via 𝗠𝗖𝗣 👇 If you are building RAG systems and packing many data sources for retrieval, most likely there is some agency present at least at the data sour…

I have been building and operating Agentic AI Systems for the past few years and the same patterns keep emerging. 👇 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗗𝗿𝗶𝘃𝗲𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 is the most reliable way…

𝗠𝗖𝗣 plus 𝗔𝟮𝗔, here is how they complement each other 👇 Protocol wars continue to rage, let's understand how Googles A2A (Agent2Agent) protocol is different from MCP and how they complement eac…

You must know these 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 as an 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿. If you are building Agentic Systems in an Enterprise setting you will soon discover that…

Fundamentals of a 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲. With the rise of GenAI, Vector Databases skyrocketed in popularity. The truth - Vector Databases are also useful outside of a Large Language Model con…

My latest newsletter episode on 𝗦𝘁𝗮𝘁𝗲 𝗼𝗳 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝗻 𝟮𝟬𝟮𝟲 is out. Most agent failures come from poor context, not weak models. Eight months after the Manus a…

5.3 posts/week
Posts / Week
1.5 days
Days Between Posts
1
Total Posts Analyzed
HIGH
Posting Frequency
272.6666666666667%
Avg Engagement Rate
STABLE
Performance Trend
280
Avg Length (Words)
HIGH
Depth Level
ADVANCED
Expertise Level
0.78/10
Uniqueness Score
YES
Question Usage
0%
Response Rate
Writing style breakdown
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The 𝗥𝗔𝗚 𝗧𝗿𝗶𝗮𝗱 is the foundation of evaluating your LLM applications. 👇
If you are moving beyond simple prompts, you need a way to measure if your retrieval is actually working. Most developers stop at "it looks okay," but that doesn't scale in production.
𝟭. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲: Is the retrieved context actually useful to answer the user query?
𝟮. 𝗚𝗿𝗼𝘂𝗻𝗱𝗲𝗱𝗻𝗲𝘀𝘀: Is the answer derived 𝗼𝗻𝗹𝘆 from the retrieved context, or is the model hallucinating?
𝟯. 𝗔𝗻𝘀𝘄𝗲𝗿 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲: Does the final response actually address the user's original intent?
➡️ You can pinpoint exactly where the system fails (Retrieval vs. Generation).
➡️ You can iterate on your chunking strategy without guessing.
➡️ You build trust with your stakeholders by showing hard metrics.
❗️ 𝗧𝗶𝗽: Don't try to automate everything on day one. Start with manual "Golden Sets" and then move to LLM-as-a-judge for scaling your Evals.
I'll be diving deeper into these patterns in my upcoming workshop on 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀.
You can join the waitlist here: https://lnkd.in/example
How are you measuring the performance of your RAG systems today? Let me know in the comments! 👇
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