DEV Community: IshqDehlvi

The architecture of persistent AI memory: Beyond simple vector search

IshqDehlvi — Wed, 25 Mar 2026 18:22:22 +0000

Most RAG implementations are lazy. Developers just throw embeddings into a vector database and hope for the best. That is not how you build a production-grade AI agent with long-term memory.

The standard approach of 100% semantic similarity is fundamentally flawed. It lacks the human element of situational awareness and chronological priority.

If a user mentioned their favorite coffee once three years ago, but just told the AI about a critical life event ten minutes ago, a basic vector search treats them with equal weight — if the "coffee" embedding happens to be a closer match to the current query.

What I Built Instead

I solved this in the https://wtmf.ai codebase by implementing a Hybrid Scoring Algorithm in our memory-context utility.

We moved away from a singular focus on cosine similarity and engineered a multi-dimensional ranking system that weighs context like a biological brain would.

The Scoring Model Breakdown

Semantic Similarity (70%)

We use OpenAI text-embedding-3-small and pgvector to handle the heavy lifting of conceptual matching.

1536 dimensions of context provide the foundation
But they are no longer the final word

Recency Decay (15%)

We implemented a time-decay function directly in the SQL query.

Calculates the epoch difference between now and the memory's creation date
Exponentially boosts anything from the last 30 days

This ensures that the AI stays "in the moment" and doesn't hallucinate context from months-old conversations.

Explicit Importance (15%)

Not all sentences are created equal.

During memory extraction, we assign an importance score based on the critical nature of the information.

User’s name or medical allergy → High importance
Casual comment about the weather → Low importance

This ensures high-impact data resurfaces, regardless of semantic proximity or age.

The Outcome

The result is a memory recall system where the AI doesn't just retrieve data — it prioritizes it.

It understands that human context is a mix of:

What you said
When you said it
How much it actually matters

Final Thought

This is the difference between:

A chatbot that reads a database
And an agent that actually remembers you

Stop building basic RAG. Start engineering memory.

Architecting a Real-Time Crisis Intervention Layer for AI Chats

IshqDehlvi — Tue, 24 Mar 2026 11:55:34 +0000

I believe that as AI developers, our responsibility extends beyond just shipping features. We are building systems that interact with humans in their most vulnerable moments.

I recently implemented a critical safety module for my platform, WTMF (https://wtmf.ai/), that detects and intervenes during crises, specifically focusing on self-harm and suicidal ideation.

In this post, I will break down the multi-layer architecture I built to handle these sensitive situations with zero latency and high empathy.

The Strategy: Detect, Flag, and Re-Steer

A standard AI response might not be sufficient when a user expresses distress. My solution involves three core layers:

Identification: High-speed pattern matching for distress signals.
Behavioral Transformation: Dynamic modification of the LLM's system prompt on the fly.
Administrative Visibility: Persistent database flagging for human review.

1. High-Precision Crisis Detection

I avoided complex asynchronous NLP models for the initial detection to ensure zero latency. Instead, I used a battle-tested regular expression approach that flags the session immediately.

const selfHarmRegex = /\b(suicide|kill myself|end my life|want to die|self harm|cut myself|overdose|jump off|hang myself|hurt myself|no reason to live|slit|slit my wrists|knife)\b/i;
const isSelfHarm = selfHarmRegex.test(message);

2. Dynamic LLM Re-Steering

The most impactful part of this module is how it changes the AI's behavior. When a distress signal is detected, I don't just block the message. I re-steer the AI.

I dynamically inject a CRITICAL INSTRUCTION into the LLM's system prompt. This ensures that the response isn't just about the persona, but about human safety.

if (isSelfHarm) {
  systemPrompt += "\n\nCRITICAL INSTRUCTION: The user has expressed thoughts of self-harm or suicide. You MUST be extremely supportive, empathetic, and gently encourage them to seek professional help or connect with loved ones. Do not give medical advice, but be a gentle, safe space for them.";
}

This transforms the AI from a standard assistant into a supportive, empathetic listener that prioritizes life over logic.

3. Persistent Safety Flagging

Finally, the session is flagged in the backend database. This allows administrators to prioritize these sessions for human intervention and support.

if (isSelfHarm) {
  updateData.isFlagged = true;
  updateData.flagReason = "self_harm";
}

await db.update(chats).set(updateData).where(eq(chats.id, chat.id));

Conclusion

Building with AI is powerful, but building with safety is essential. I am committed to ensuring that WTMF doesn't just process data, but handles human emotions with the care they deserve.

Check out WTMF: https://wtmf.ai/

What safety layers are you building into your AI projects? I would love to hear your thoughts in the comments.

How HarGharPG Is Digitizing PG Management in India

IshqDehlvi — Thu, 22 Jan 2026 08:40:17 +0000

Managing a Paying Guest (PG) or co-living space in India involves juggling tenants, bed inventory, rent cycles, notices, mess menus, KYC documents, and more. Traditionally, many owners handle this with Excel sheets, paper registers, or scattered WhatsApp messages — which quickly becomes chaotic.

In this post, I’m breaking down what HarGharPG is, what it does, and how it helps PG owners run their business more efficiently.

🧠 What Is HarGharPG?

HarGharPG is a PG management software and web app designed specifically for Indian PG and co-living operators. It helps owners automate daily operations like tenant records, rent collection, occupancy tracking, invoicing, and communication — all from a unified dashboard.

HarGharPG is positioned as one of India’s top PG management solutions, trusted by owners across multiple cities and properties.

🛠 Core Things HarGharPG Helps You Do

✅ 1. Manage Rooms & Beds

Set up floors, rooms, and beds with real-time occupancy tracking — critical for shared living spaces.

✅ 2. Tenant Management

Record tenant details, KYC documents, and allocation to specific beds. You can easily search and filter tenant profiles.

✅ 3. Rent & Revenue Tracking

Track monthly rent payments and dues, record paid amounts, and view overall revenue metrics. Automated reminders can be sent to tenants via WhatsApp or email.

✅ 4. Billing & Invoices

Generate professional, GST-compliant invoices for tenants and share them electronically.

✅ 5. Multi-Language Support

The platform supports major Indian regional languages so owners and tenants can use the system in their preferred language.

✅ 6. Public PG Microsite

Each property gets a tenant-facing site to view notices, mess menus, PG rules, and submit support requests — all under a custom subdomain.

✅ 7. Central Dashboard & Analytics

Visualize occupancy stats, rent collections, security deposits, and key performance metrics in one place.

✅ 8. Multi-Property Management

Manage multiple PGs from a single account with property-wise switching and centralized tenant tracking.

✅ 9. Document Storage via Google Drive

Store tenant KYC and important files securely using your own Google Drive account.

📈 Extra Capabilities

HarGharPG also offers:

Automated WhatsApp + email notices
Mess menu scheduling
Support ticketing for tenants
Staff management and role-based access
Guided onboarding wizard

All designed to reduce administrative effort and centralize operations.

📊 What They Maintain Behind the Scenes

From the product side, HarGharPG maintains:

A bed-level inventory and occupancy tracking system
Tenant CRM with document management
Recurring rent tracking and reminders
Invoice generation and financial reports
Public tenant interactions (notices, menus, support)

Multi-property dashboard and analytics
These capabilities help PG owners replace manual processes with a structured, data-driven workflow.

💡 Why This Matters

HarGharPG is a great example of vertical SaaS — a tool tailored for a specific industry and geography. It’s built around the everyday realities of Indian PG businesses, like:

High tenant turnover
Diverse languages
Multiple rent channels (cash, UPI, bank, wallets)
Frequent notices and menus

By focusing on these niche operational needs, the platform adds real value beyond generic property management tools.

🚀 Final Thoughts

If you’re building tools for SMB operations, especially in markets like India, HarGharPG is worth studying — not because it’s perfect, but because it shows how domain focus + automation + simple UX can transform a traditionally manual business.