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…

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Agentic RAG Architecture8 days ago

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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…

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Agentic AI Development10 days ago

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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…

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AI Agent Protocols15 days ago

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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…

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Agentic AI Workflows11 days ago

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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…

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Vector Databases4 days ago

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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…

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AI Agents12 days ago

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Topics & Content Focus

Primary Topics

Production-grade Agentic AI systems (from prototype to monitored production)Evaluation-driven development for LLM applications (evals, tracing, observability loops)Retrieval architecture for GenAI (Vector DBs, RAG pipelines, reranking, metadata filtering)Composable AI platform integration patterns (MCP-enabled multi-domain retrieval and governance)

Secondary Themes

LLMOps skill-building and course-based education productizationKubernetes foundations for AI engineers (deployment and operational primitives)Security/compliance boundaries for agent access to data domainsFinetuning vs topology/prompt changes as levers for improvement

Industry Focus

LLM/GenAI engineering for B2B productsAI infrastructure and platform engineering (RAG, vector search, observability, deployment)Developer education for production AI (courses, newsletters, conference presence)

Content Categories

Step-by-step technical primers (conceptual + operational walkthroughs)System design / architecture playbooks (pipelines, components, integration layers)Reusable frameworks & templates (processes that readers can adopt)Resource distribution (free articles/newsletter funnel)Event-based community marketing (conference activation + exclusives)

Performance Insights

272.6666666666667%

Avg Engagement Rate

STABLE

Performance Trend

Best Performing Topics

Agentic RAG architecture with MCP (multi-source retrieval + governance)Evaluation-driven development loops for agentic systems (template/process)Practical retrieval infrastructure explanations (vector DB fundamentals with actionable mechanics)

Virality Signals

Shares correlate with 'architecture-as-a-map' posts (clear pipelines that teams can adopt)Novel terminology/standard introduction + concrete placement in the stack (e.g., MCP in RAG) drives re-sharingFramework/template posts invite bookmarking and team-internal forwardingPure promotional/event posts underperform compared to builder-oriented playbooks

Structure & Quality

280

Avg Length (Words)

HIGH

Depth Level

ADVANCED

Expertise Level

0.78/10

Uniqueness Score

Common Hooks

Direct utility promise to a specific builder persona (e.g., 'If you are building X…')Concept framing as a 'fundamentals' explainer that quickly becomes implementation-orientedNew/novel integration angle (introducing a specific mechanism/standard and where it fits)Authority-through-experience opener ('I’ve been building/operating… patterns keep emerging')

Common Endings

Experience-question to trigger practitioner comments ('Are you using…?')Invite to share implementation details ('Let me know…')Single-link handoff to deeper content (article/course/tickets)

Value Delivery Methods

Operationalizes complex GenAI concepts into implementable steps (not just opinions)Bridges prototype-to-production gap (evals, monitoring, iteration loops)Teaches system boundaries (metadata filtering, reranking, governance/security per domain)Provides ready-to-use learning pathways (free K8s series, newsletter, course early access)

Formatting Style

Heavy use of bold section headers and emphasized keywordsNumbered sequences (pipelines, step frameworks) as the core clarity deviceArrow bullets (➡️) and checkmarks (✅) to signal structure and claimsShort headline + dense instructional body (mini-lecture format)

Audience & Tone

YES

Question Usage

Response Rate

Detected Tone

Pragmatic systems-builderClinical but accessible educatorArchitecture-first thinkerConfident operator (production mindset)semi-formalmixed

Interaction Style

Practitioner-to-practitioner prompting (asks for real-world usage patterns)Community-building through shared tooling standards and event meetupsFeedback loops framed as collective learning ('tell me your experience')

Community Building Signals

Conference presence as relationship accelerant (in-person touchpoints + exclusives)Newsletter and course ecosystem as ongoing learning communityRepeated prompts for peer implementation stories to build comment-driven social proof

Writing Style Patterns

Content Strategy

Hook: Direct utility promise to a specific builder persona (e.g., Tone: semi-formalCTA: Discussion CTA (ask for tooling/experience in comm

Writing style breakdown

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.

Here is how you should structure your 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗟𝗼𝗼𝗽

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?

𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀

➡️ 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! 👇

Aurimas Griciūnas

Warm Analysis

Performance Overview

Top Posts by Engagement

Posting Patterns & Frequency

Best Performing Days

Best Performing Times To Post

Topics & Content Focus

Primary Topics

Secondary Themes

Industry Focus

Content Categories

Performance Insights

Best Performing Topics

Virality Signals

Structure & Quality

Common Hooks

Common Endings

Value Delivery Methods

Formatting Style

Audience & Tone

Detected Tone

Interaction Style

Community Building Signals

Writing Style Patterns

Content Strategy

Here is how you should structure your 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗟𝗼𝗼𝗽

𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀