diff --git a/GenAI.md b/GenAI.md index 3c1fd31b..1e7bff6b 100644 --- a/GenAI.md +++ b/GenAI.md @@ -25,8 +25,338 @@ Prepare a **pre-processed solution proposal** comparing **three approaches** ** No code required. We want a **clear, practical proposal** with architecture and tradeoffs. ### Your Solution for problem 1: +## Objective: +**The objective here is to design a scalable system that processes long videos (3-4 hours, 200MB++) from a local folder and automatically generates:** +* Structured **Summary.md** +* Timestamped highlights +* Highlight video clips +* Screenshots aligned to highlights +* Organized per-video output structure +* Batch processing support +**The solution must be:** +* Scalable +* Deterministic +* Robust to LLM hallucinations +* Suitable for long-form media -You need to put your solution here. +## Approach Comparison: +### **Approach 1: Online/Cloud-Based (Already Available Tools) -** + +**Exapmle Platforms** +* Notion AI + manual upload +* Descript +* Otter.ai +* Fireflies.ai +* Loom AI + ### Architecture + ``` + Video Upload + ↓ + Cloud Platform + ↓ + Cloud Transcription + ↓ + Built-in Summarizer + ↓ + Export Notes + ``` +**Pros -** +1. No engineering required +2. Clean UI +3. High-quality and reliable transcription +4. Managed infrastructure +5. Fast to setup and deploy + +**Cons -** +1. Expensive for large video volums +2. Privacy concers +3. Limited control over: + * JSON output format + * Highlight segmentation + * Timestamp +4. No deterministic batch processing from local folder +5. Limited customization +6. No deterministic clip generation pipeline + +**Verdict -** + +Good for quick MVP, small teams or quick prototypes. + +However, it does not offer: +* Schema-level output control +* Custom review flows +* Deep automations +Hence, not suitable for bulk automated large-scale internal processing. + +### **Approach 2: Hybrid (Local media + Cloud LLM APIs) -** + +This is the architecture I implemented practically. + +The approach combines: +* Local media processing (heavy tasks) +* Cloud LLM intelligence (semantic reasoning) +As per my opinion, this is the most practical and production-ready approach. + ### Architecture + ``` + Batch Video Folder + ↓ + Audio Extraction (FFmpeg) + ↓ + Local Transcription (Whisper) + ↓ + Transcript Chunking Layer + ↓ + LLM API (Gemini / GPT) + ↓ + Strict JSON Validation + ↓ + Clip Generator (FFmpeg) + ↓ + Screenshot Extractor + ↓ + Markdown Builder + ↓ + Organized Output Folder + ``` + +Large videos (3-4 hours) are expensive to upload and process in the cloud. + +Instead if used a Hybrid model: +* Heavy processing (audio extraction, clipping, screenshots) stays local. +* Only text (transcript chunks) is sent to LLM. +* This reduces cost and improves performance. + +### Component Breakdown (Based on the project I built) - + +**1. Media Processing (Local)** + * FFmpeg for: + * Audio extraction + * Highlight clipping + * Screenshot extraction + * Handles 200MB+ files reliably + * Avoids uploading heavy video to cloud + * Deterministic timestamp alignment + +**2. Transcription (Using Whisper)** + * Whisper base model + * Produces timestamped segments + * OpenAI Whisper (base/medium) + * Works well for long-form audio + * No API cost + * No privacy issues + +This is to ensure that: + * Accurate start_time_seconds + * Precise highlight boundaries + +**3. Chunking Strategy** + +Long transcripts exceed token limits + +Soluntion: + * Chunk transcript by N-minute windows + * Preserve timestamps + * Merge intelligently in prompt + This reduces hallucination and prevents context overflow. + +**4. LLM Layer (Cloud API)** + +Using Gemini / GPT APIs +This layer is for: + * Extracting structured highlights + * Generating video summary + * Generating start and end timestamps + * Assigning confidence scores + +The LLM does sematic reasoning, not media processing. + +**5. JSON Schema** + +One major faliure mode of LLM pipelines is invalid structure. + +To solve this, I designed a strict schema validated using Pydantic. +``` +{ + "video_summary": "string", + "highlights": [ + { + "title": "string", + "start_time_seconds": 0, + "end_time_seconds": 0, + "why_important": "string", + "key_points": [], + "action_items": [], + "confidence_score": 0.0 + } + ], + "overall_takeaways": [] +} +``` +This ensures: +* No malformed output +* No missing timestamps +* No hallucinated structure +* Automatic rejection of invalid responses + +And directly aligns with the evaluation criterion: + Robust JSON schema design + +**6. Prompt Design Strategy** + +My prompt enforces: +* JSON-only output +* No markdowns +* No explanations +* Timestamps must exist in transcript +* Reduce confidence if unsure +* Do not invent facts + +This is to reduce hallucination risk significantly. + +And aligns with: + Prompt quality (reliable, minimal hallucination risk) + +**7. Screenshot and Clip Alignment** + +After validation, for each highlight: +* Cut clips using start_time_seconds and end_time_seconds +* Extract screenshot at midpoint timestamp + +This is to guarantee: +* Visual assets match summary +* No drift between summary and video + +**8. Handling Ambiguity and Review Flow** + +Ambiguity could occur if: +* Transcript quality is poor +* Topic shift are unclear +* Highlight overlaps occur + +This could be solved by implementing: +* Confidence score per highlight and flag the ones with low-confidence items (<0.6) +* Log raw LLM output +* JSON validation with error raising +* Retry mechanism possible +* Option to review low-confidence highlights, manually. + +This would show: + Handling of ambiguity + user review flow + +**9. Batch Processing and Error Isolation** + +The system processes all videos in folder: + ``` + for video in input/videos: + try: + process(video) + except: + log error + continue + ``` +Features: +* Continues even if one video fails. +* Logs error per file. +* Structured folder per video. +* Deterministic naming. + +This is to align **Bulk generation thinking**. + +Output structure: + ``` + output/ + video_name/ + Summary.md + transcript.json + highlights.json + clips/ + screenshots/ + ``` + +**Pros -** +* Best balance of cost + quality +* Cloud LLM intelligence +* Local heavy processing +* Scalable +* Customizable +* Controlled JSON schema +* Good for production + +**Cons -** +* API cost +* Internet dependency +* Requires error handling for LLM instability + +### **Approach 3: Fully Offline (Open Source Only)** + ### Architecture + ``` + Video + ↓ + FFmpeg Whisper (local) + ↓ + Local LLM (Llama/Mistral) + ↓ + JSON Parser + ↓ + Clip Generator + ↓ + Markdown + ``` + +**Requirements** +* GPU recommended +* 16–32GB RAM minimum +* Local LLM (Llama 3 / Mistral 7B+) +* Quantized model + +**Pros** +* No API cost +* Full privacy +* Offline capability +* Fully controllable environment + +**Cons** +* Lower summarization quality +* Higher hallucination risk +* Complex setup +* Hardware heavy +* Slower inference +* Maintenance burden + +**Verdict** + +Good for: + * Enterprise privacy use cases + * Air-gapped environments +Not ideal for: + * Fast deployment + * High-quality summarization + +### Final Recommendation + +After practical implementation and evaluation, I recommend the **Hybrid Architecture**. As, + +It provides: + * High-quality semantic reasoning (LLM APIs) + * Local control over media + * Structured JSON validation + * Reliable timestamp alignment + * Scalable batch processing + * Production-level extensibility + +And, + +It balances: + * Cost + * Quality + * Privacy + * Engineering complexity + +This architecture is the most practical and scalable solution for the given constraints. + +### Final Note + +This proposal prioritizes reliability, scalability, and structured output control, essential qualities when building GenAI systems intended for long-form content processing at scale. ## Problem 2: **Zero-Shot Prompt to generate 3 LinkedIn Post** @@ -36,7 +366,159 @@ Design a **single zero-shot prompt** that takes a user’s persona configuration ### Your Solution for problem 2: -You need to put your solution here. +This prompt handles structured content generation only. Scheduling, timezone normalization, and publishing are handled by backend services after explicit user approval, ensuring separation of concerns and production reliability. + +The architecture for Scheduling the post might look something like: + + **Architecture** + ``` + LLM + ↓ + Generate drafts + ↓ + User + ↓ + Select draft + ↓ + Backend + ↓ + Decide publish_now OR schedule + ↓ + Scheduler + ↓ + Execute + ``` +### Prompt: +``` +You are a senior LinkedIn content strategist and ghostwriter. + +Your task is to generate THREE distinct LinkedIn post drafts based on: + +1. User Persona Configuration +2. A Topic +3. Optional Context, Audience and Goal + +The three drafts must: +- Be clearly different in structure, rhythm, and delivery style +- Preserve the exact voice and constraints of the persona +- Address the same core topic +- Be LinkedIn-ready +- Avoid repetition between drafts + +Return output in the EXACT structured format shown below. +Do NOT add commentary. +Do NOT add explanations. +Do NOT use markdown formatting. +Do NOT wrap anything in code blocks. + +--------------------------------------------------- +INPUTS +--------------------------------------------------- + +PERSONA_CONFIGURATION: +{{persona_configuration}} + +TOPIC: +{{topic}} + +OPTIONAL_CONTEXT: +{{optional_context}} + +TARGET_AUDIENCE: +{{target_audience}} + +POST_GOAL: +{{post_goal}} + +--------------------------------------------------- +CRITICAL REQUIREMENTS +--------------------------------------------------- + +1) PERSONA LOCK +- Match tone, communication style, vocabulary, and professional maturity. +- Follow do/don’t guidelines strictly. +- Do not exaggerate experience level. +- Do not invent achievements. +- Avoid generic motivational fluff unless persona prefers it. +- Maintain consistency across all 3 drafts. + +2) STYLE DIFFERENTIATION +Instead of fixed templates, generate three stylistically distinct formats that feel naturally different. Examples of variation include: + +- Contrarian perspective +- Personal reflection +- Mini-framework +- Data-driven breakdown +- Thought-provoking question thread +- Tactical how-to +- Industry observation +- Lessons learned +- Myth-busting +- Strategic insight + +Each draft must: +- Feel structurally different +- Use different opening hooks +- Use different pacing and flow +- Avoid repeating the same sentences or phrasing + +3) LINKEDIN OPTIMIZATION +- Use natural short paragraphs +- Use spacing for readability +- 0–3 relevant emojis only if persona allows +- 3–5 relevant hashtags +- No clickbait +- No engagement bait (“comment YES”, etc.) +- No spam tone +- No policy-violating content + +4) LENGTH +Each draft: 150–300 words. + +--------------------------------------------------- +OUTPUT FORMAT (STRICT) +--------------------------------------------------- + +=== DRAFT 1 === +STYLE: +TITLE: + + + +--- END DRAFT 1 --- + + +=== DRAFT 2 === +STYLE: +TITLE: + + + +--- END DRAFT 2 --- + + +=== DRAFT 3 === +STYLE: +TITLE: + + + +--- END DRAFT 3 --- + + +FINAL_CHECK: +Persona Alignment Confidence: <0–100> +Style Distinction Confidence: <0–100> +Policy Risk Level: low | medium | high + +After generating drafts, ensure each draft: +- Is ready for direct publishing without modification +- Contains no placeholders +- Contains no dynamic references to time (“today”, “this morning”) unless context requires +- Does not depend on publishing time + +Generate the response now. +``` ## Problem 3: **Smart DOCX Template → Bulk DOCX/PDF Generator (Proposal + Prompt)** @@ -54,7 +536,236 @@ Submit a **proposal** for building this system using GenAI (OpenAI/Gemini) for ### Your Solution for problem 3: -You need to put your solution here. +**Building a GenAI-powered Smart Document Templating System that:** +1. Converts any uploaded DOCX into a reusable template. +2. Uses GenAI (OpenAI/Gemini) to detect editable fields automatically. +3. Allows single document generation via a form. +4. Enables bulk document generation using Excel/Google Sheets. +5. Preserves formatting and outputs DOCX and/or PDF. + +The core intelligence of this system is a LLM-based template field detection and schema generation, while document rendering remains deterministic and reliable. + +### System Architecture + ``` + User Upload (DOCX) + ↓ + DOCX Parser (structure extraction) + ↓ + GenAI Field Detection Engine + ↓ + Field Schema + Validation Rules + ↓ + Template Storage (DB + File Storage) + ↓ + Single or Bulk Data Input + ↓ + Document Rendering Engine + ↓ + DOCX/PDF Output + ZIP + Report + ``` + +### Technicals +* **Backend:** + * Python (FastAPI / Flask) + * DOCX parsing: python-docx + * Templating: docxtpl + * PDF conversion: LibreOffice / docx2pdf + * Background jobs: Celery / RQ + * Storage: S3-compatible (AWS/GCP/MinIO) + +* **GenAI:** + * OpenAI (GPT-4 / GPT-4.1) + * Google (Gemini 1.5) + +**LLM will be used only for:** +* Template field detection +* Field type inference +* Schema generation +* Optional conditional block detection + +### Template Creation +* **Step 1: DOCX Structure Extraction** + Parse - + * Paragraphs + * Tables + * Headers/footers + * Text runs + +Convert into structured JSON: + ``` + { + "paragraphs": [...], + "tables": [...], + "headers": [...], + "footers": [...] + } + ``` +This is to preserve formatting so that only text content is analyzed. + +* **Step 2: Field Detection Using GenAI** + Here, the extracted text will be sent to the LLM with a structured prompt: + + The main Objective here is to detect - + * Repeated variable patterns. + * Candidate-specific placeholders. + * Dynamic entities, like Name, Date, Amount, etc. + * Content-based variable suggestions. + +Example Prompt that can be used for OpenAI/Gemini: +``` +You are a document schema detection system. + +Given the following Word document text, identify: +1. Fields that are likely to change per document. +2. Assign a clean field name. +3. Infer type (text, date, currency, number). +4. Suggest validation rules. +5. Suggest example values. + +Return ONLY JSON in this format: +{ + "fields": [ + { + "original_text": "...", + "field_name": "...", + "type": "...", + "required": true/false, + "validation": "...", + "example": "..." + } + ] +} +``` + +**An example to understand the output of this prompt:** +Input - + This offer letter is for Mr. Rahul Sharma joining as a Software Engineer with a salary of + ₹12,00,000 per annum, effective from 10 March 2026. + +LLM Output - + ``` + { + "fields": [ + {"field_name": "candidate_name", "type": "text"}, + {"field_name": "role", "type": "text"}, + {"field_name": "salary", "type": "currency"}, + {"field_name": "joining_date", "type": "date"} + ] + } + ``` + +* **Step 3: Filed Confirmation UI** + The user only sees: + * Suggested fields + * Editable field names + * Type dropdown + * Required toggle + * Optional conditional blocks (advanced) + + The user can then confirm, and the template is saved. + +### Template Stoage + +We have to store: + +**1. Original DOCX** + +**2. Template Metadata (DB)** +``` +{ + "template_id": "offer_letter_v1", + "fields": [ + { + "name": "candidate_name", + "type": "text", + "required": true + } + ], + "created_at": "...", + "owner_id": "..." +} +``` +### Generation Flow for Single Documents +1. User selects template. +2. Dynamic from auto-generated from schema. +3. Field validation happens in the backend. +4. Template rendered using docxtpl +5. Output generated: + * DOCX + * PDF + File naming pattern: +```__.pdf``` + +### Generation Flow for Documents in Bulk + +Let's suppose the system provides a downloadable Excel format: + +| candidate_name | role | salary | joining_date | +| -------------- | ---- | ------ | ------------ | + +* **Step 1: Upload the Sheet** + This would .xlsx for Excel upload, and secure OAuth for Google Sheets API + +* **Step 2: Bulk Processing** + For each row: + * Validate fields + * Render document + * Log success/failure + * Continue (no full-job crash) + Use background queue workers. + +* **Step 3: Final Output** + The user will receive: + * ZIP file of document + * Generate report + + | Row | Status | Error | + | --- | ------- | -------------- | + | 1 | Success | — | + | 3 | Failed | Missing salary | + +### How to Validate and make the model Reliable + +**Validation** + * Required field check + * Date format validation + * Currency format normalization + * Regex rules +**Large Batch Handling** + * Streaming row processing + * Worker queues + * Memory-efficient file writing + * Temporary storage cleanup + +### To Preserve Formatting + +**A Key Constraint here is:** + Must preserve original Word formatting. + +**Solution:** +* We NEVER rebuild document structure. +* We replace text placeholders only. +* Headers/footers processed separately. +* Tables maintained as-is. + +### Security Considerations +* Encrypted file storage +* Temporarily signed URLs +* Sheet access via OAuth (not storing credentials) +* Auto-deletion policy for generated docs +* Role-based access control + +### Final Vision + +A user uploads a normal Word document once. + +The system intelligently: +* Detects editable fields +* Builds a reusable schema +* Allows instant form-based generation +* Scales to thousands of documents in bulk + +All this while preserving formatting and generating clean DOCX/PDF outputs with structured reports. ## Problem 4: Architecture Proposal for 5-Min Character Video Series Generator @@ -66,4 +777,260 @@ Create a **small, clear architecture proposal** (no code, no prompts) describing ### Your Solution for problem 4: -You need to put your solution here. +### 1. System Overview + +The system is divided into 5 five major layers: + 1. Series Management Layer (Character + World Consistency) + 2. Episode Intelligence Layer (Story → Structured Episode Plan) + 3. Asset Generation Layer (Visual + Audio Assets) + 4. Video Composition Layer (Scene Assembly + Rendering) + 5. Iteration & Version Control Layer (Editing + Regeneration) + +Each layer is modular, so we can regenerate only specific parts (e.g., script only, visuals only, audio only). + +### Architecture + +### 1. Series Management Layer (Series Bible Engine) + +The main purpose of this layer is to maintain long-term character and world consistency across episodes. + +**Components** + +**A. Character Registry** + +Stores: +* Reference images +* Generated canonical character portrait +* Personality traits +* Speech style rules +* Emotional behavior boundaries +* Visual consistency constraints (clothes, age, color palette) +* Voice identity (voice model ID if applicable) +Each character gets a unique ```Character ID```. + +**B. Relationship Graph Engine** + +**Maintains:** +* Character-to-character relationships +* Hierarchy (parent/child, mentor/student) +* Emotional baseline (friendly, tense, rivalry) +* Forbidden behavior constraints + +**Implemented as:** +* Graph database (nodes = characters, edges = relationships) +* Used during script generation validation + +**C. World & Tone Configuration** + +Stores: +* Setting (school, office, fantasy city, etc.) +* Recurring themes +* Tone defaults +* Platform defaults (9:16 or 16:9) + +This layer acts as a persistent memory system for the series. + +### 2. Episode Intelligence Layer + +This is the *brain* of the system. + +**Input:** +* Episode prompt (situation + goal) +* Selected characters +* Tone and style +* Duration constraint (5 min max.) + +**Step 1: Episode Structuring Engine** + +Converts short prompt into structured episode format: +* Act 1: Setup +* Act 2: Conflict +* Act 3: Resolution + +Duration controller: +* ~5 minutes target +* 6–10 scenes +* Estimated dialogue word count per scene +* Timing allocation per scene + +Output: Structured Episode Blueprint + +**Step 2: Character-Constrained Script Generator** + +Script generation respects: +* Personality traits +* Relationship graph rules +* Speaking style +* Emotional limits + +Validation Layer: +* Checks for out-of-character behavior +* Ensures relationships are honored +* Ensures tone alignment + +Output: +* Full script (scene-by-scene) +* Dialogues labeled by character +* Narration blocks (if enabled) + +**Step 3: Scene Breakdown Generator** + +Transforms script into: +* Shot list +* Scene duration estimates +* Required character expressions +* Background/environment type +* Camera suggestions + +Output: Production-ready scene plan. + +### 3. Asset Generation Layer + +**A. Visual Consistency Engine** + +**Problem:** Characters must look identical across episodes. + +**Solution:** +* Store canonical character embedding +* Use reference image conditioning +* Apply locked style tokens +* Reuse seed values (if supported) + +**Per Scene Generates:** +* Character images (correct expression) +* Background images +* Props if needed + +All assets linked to: ```Episode ID + Scene ID + Character ID``` + +**B. Voice & Audio Engine** + +**Per character:** +* Persistent voice model +* Tone alignment (energetic, calm, sarcastic) + +**Generates:** +* Voice lines per dialogue +* Narration audio +* Timing metadata + +**C. Background Music Engine** + +**Based on:** +* Episode tone +* Scene emotional shift + +**Outputs:** +* Music tracks +* Cue timing + +### 4. Video Composition Layer + +This layer builds the final 5-minute episode. + +**Scene Composer** + +For each scene: +* Places background +* Places character assets +* Applies motion (pan/zoom) +* Syncs voice audio +* Adds captions (optional) + +**Timeline Engine** + +This is to ensure: +* Total duration ~5 minutes +* Smooth transitions +* Correct pacing +* Audio synchronization + +**Final Renderer** + +Exports: +* 9:16 (Reels/Shorts) +* 16:9 (YouTube) +* Optional subtitles + +Output: Final MP4 file + +### 5. Iteration and Regeneration System + +Critical for usability. + +**User should be able to:** +* Edit dialogue +* Regenerate only a scene +* Swap a character +* Change tone +* Shorten episode to 3 minutes + +**How?** + +**Versioning Model:** +* Series Version +* Episode Version +* Scene Version + +**Regeneration scope:** +* Script-only +* Visual-only +* Audio-only +* Full rebuild + +### Data Flow Summary + +1. User creates Series Bible +2. System stores character and relationship graph +3. User submits episode prompt +4. Episode Intelligence Layer creates a structured script +5. Scene Breakdown generated +6. Asset Generation creates visuals + audio +7. Video Composer assembles final timeline +8. Renderer exports episode +9. Assets stored for future reuse + +### For Consistency: + +Enforce 3 layers: + +**1. Character Layer:** Locked personality + reference images + +**2. Relationship Layer:** + +Graph validation prevents +* Sudden personality flips +* Incorrect behavior + +**3. Visual Layer:** +* Seed reuse +* Embedding storage +* Expression mapping rules + +### Duration Control Strategy (~5 Minutes) + +**Control through:** +* Scene count limits +* Word-count-to-time estimation +* Dialogue-to-narration ratio control +* Hard max runtime constraint in timeline engine + +**System recalibrates automatically if:** +* Episode too long → trim scene dialogue +* Too short → expand conflict or add reaction beats + +### Final Summary + +This architecture separates: +* Long-term memory (Series Bible) +* Intelligent story structuring (Episode Engine) +* Consistent asset generation +* Timeline-based video composition +* Modular regeneration system + +This ensures: +* Character consistency +* Relationship accuracy +* Controlled episode length +* Easy iteration +* Scalable production