README interview hub bullets + new Trendshift badge + by-topic courses link; add ASCII diagrams to roadmaps

- Interview Prep section leads with the hub, hyperlinked bullet per role, and folds
  in the 60-questions bank as shared fundamentals
- Update the Trendshift badge; add a clear link to the by-topic All Free Courses list
- Add concept ASCII diagrams to the LLM foundations, agents, and RAG roadmaps

README.md

@@ -2,7 +2,7 @@
 
 Generative AI is moving fast, and this repository is a comprehensive hub for generative AI research, courses, interview materials, notebooks, and more. Everything is now organized around one question: what do you want to do?
 
-<a href="https://trendshift.io/repositories/7663" target="_blank"><img src="https://trendshift.io/api/badge/repositories/7663" alt="aishwaryanr%2Fawesome-generative-ai-guide | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+<a href="https://trendshift.io/repositories/7663?utm_source=trendshift-badge&amp;utm_medium=badge&amp;utm_campaign=badge-trendshift-7663" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/trendshift/repositories/7663/daily" alt="aishwaryanr%2Fawesome-generative-ai-guide | Trendshift" width="250" height="55"/></a> <a href="https://trendshift.io/repositories/7663?utm_source=repository-badge&amp;utm_medium=badge&amp;utm_campaign=badge-repository-7663" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/repositories/7663" alt="aishwaryanr%2Fawesome-generative-ai-guide | Trendshift" width="250" height="55"/></a>
 
 ---
 
@@ -13,7 +13,7 @@ Pick the door that fits you. Each journey has its own 101 to 301 path.
 - 🧑‍💻 **I want to use AI in my work** → [Use AI](journeys/use.md)
 - 🏗️ **I want to build AI systems** → [Build AI](journeys/build.md)
 - 🔬 **I want to understand the research** → [Understand AI](journeys/understand.md)
-- 💬 **I'm prepping for an interview** → [Interview Prep](paths/interview-prep.md)
+- 💬 **I'm prepping for an interview** → [Interview Prep hub](interview_prep/README.md)
 - 📚 **I just want to browse every free course** → [All free courses, by topic](courses.md)
 
 ---
@@ -52,6 +52,8 @@ Free, created by [Aishwarya Naresh Reganti](https://www.linkedin.com/in/aishwary
 - **[Generative AI Genius](free_courses/generative_ai_genius/README.md)**: a no-math beginner introduction to generative AI.
 - **[Applied LLMs Mastery (2024 edition)](free_courses/Applied_LLMs_Mastery_2024/README.MD)**: an 11-week foundational course, archived as a 2024 edition. [Course website](https://areganti.notion.site/Applied-LLMs-Mastery-2024-562ddaa27791463e9a1286199325045c).
 
+For every free course (LevelUp Labs originals and vetted external), organized by topic, see the full **[All Free Courses, by Topic](courses.md)** list.
+
 ---
 
 ## :star: Top AI Tools List
@@ -62,7 +64,14 @@ Discover our favorite AI tools spanning every layer of AI application developmen
 
 ## :computer: Interview Prep
 
-Head to the **[Interview Prep path](paths/interview-prep.md)**, which wraps the [60 GenAI Interview Questions](interview_prep/60_gen_ai_questions.md) and points you at the topic pages to shore up any weak area. New: a deep **[Role-Based Interview Prep hub](interview_prep/README.md)** with a full folder per role (AI Engineer, AI PM, Forward-Deployed Engineer, AI Strategist): role overview, interview rounds, a large question bank with answers, resources, courses, and a prep plan. System-design interview drills are planned for a later build phase.
+A full **[Role-Based Interview Prep hub](interview_prep/README.md)**: pick your role and work its folder end to end (overview, interview rounds, a deep question bank with answers, resources, courses, and a prep plan).
+
+- 🏗️ **[AI Engineer](interview_prep/roles/ai-engineer/README.md)**: build LLM apps, RAG, agents, evaluation, and system design.
+- 📋 **[AI Product Manager](interview_prep/roles/ai-product-manager/README.md)**: product sense, metrics, tradeoffs, and responsible AI.
+- 🚀 **[Forward-Deployed Engineer](interview_prep/roles/forward-deployed-engineer/README.md)**: build, integrate, and deploy at the customer under ambiguity.
+- 🧭 **[AI Strategist](interview_prep/roles/ai-strategist/README.md)**: strategy, ROI, build vs buy, governance, and change management.
+
+Everyone starts with the shared **[60 GenAI Interview Questions](interview_prep/60_gen_ai_questions.md)**, then works their role. System-design interview drills are planned for a later build phase.
 
 ---
 

resources/RAG_roadmap.md

@@ -6,6 +6,14 @@ Retrieval Augmented Generation (RAG) is still one of the most common ways to put
 
 > Updated for 2026. RAG is now often agentic: the model decides what to retrieve, iterates, and sometimes skips the vector store entirely. Day 3 takes you there.
 
+```text
+The RAG pipeline:
+
+  question --> RETRIEVE --> relevant chunks --> AUGMENT the prompt --> LLM --> grounded answer
+                  |
+          vector search / keyword / hybrid
+```
+
 ## Day 1: Retrieval Foundations
 
 Start with the retrieval half of RAG: embeddings, vector search, and chunking. Get this right and the rest follows.

resources/agents_roadmap.md

@@ -6,6 +6,16 @@ Agents are how most real LLM applications get built now: a model wrapped in a ha
 
 > Updated for 2026. Agents changed more than any other area since 2024: reasoning models, the Model Context Protocol, agent harnesses, and real evaluation are now the core, so this roadmap is built around them.
 
+```text
+The loop every agent runs:
+
+   PLAN  -->  ACT (call a tool)  -->  OBSERVE (read the result)
+     ^                                         |
+     |_________________ until done ____________|
+
+An agent = a model + this loop + tools + memory + gates (the harness).
+```
+
 ## Day 1: What Is an Agent
 
 The mental model first: an agent is a model plus a harness. Understand the loop before the tools.

resources/genai_roadmap.md

@@ -6,6 +6,15 @@ If you're feeling overwhelmed by the scattered knowledge about LLMs, this roadma
 
 > Updated for 2026. The way people build with LLMs has changed: most builders never fine-tune a model, they assemble prompting, context, retrieval, and tools into an agent, then evaluate it. So the core 5 days below take you from foundations to a working, evaluated agent. Fine-tuning is here too, as an optional advanced day for when you actually need it.
 
+```text
+FOUNDATIONS --> PROMPTING --> RAG --> AGENTS & TOOLS --> EVALUATION & SHIPPING
+ (Day 1)        (Day 2)      (Day 3)   (Day 4)            (Day 5)
+ how LLMs       context      retrieval model + harness,   does it work
+ work           engineering            the loop, MCP      in production?
+
+                                            + optional advanced day: fine-tuning
+```
+
 ## Day 1: LLM and Reasoning Model Foundations
 
 **Watch (about 1 hour):**