# @mlatha_naidu on Instagram

- **Type:** Video
- **Original URL:** https://www.instagram.com/p/DZlEAvezrB6
- **Gondola URL:** https://gondola.cc/posts/66689216-mlatha-naidu-instagram
- **Thumbnail:** https://img.gondola.cc/tr:w-,h-,fo-auto/postThumbnails/4fa1fc922b.jpg
- **Posted:** 2026-06-14T20:06:49.000+00:00
- **Account Owner:** Madhulatha Pelluri (@mlatha_naidu) — https://gondola.cc/mlatha_naidu

## Caption

🚀 Operation Self Learning – Story Book 676 & 677
Today I continued building the foundation of an ABAP AI Code Review Assistant using SAP BTP Trial, CAP (Cloud Application Programming Model), CDS, SQLite, SAP Business Application Studio, and Fiori Preview.

What I built:
✅ SAP BTP Trial Environment
✅ CAP Project (abap-ai-review)
✅ CodeReviewService
✅ Reviews Entity
✅ SQLite Persistence
✅ Fiori Preview Application
✅ ABAP Review Dataset
✅ Suggestion Engine Modeling
✅ Confidence Score Modeling

Current Architecture:
ABAP Code
→ Review
→ Suggestion
→ Confidence Score
→ CAP Service
→ Fiori UI

Sample Scenario:
ABAP Code:
SELECT * FROM mara INTO TABLE lt_mara.
Review:
SELECT * should be avoided. Select only required fields.

Suggestion:
SELECT matnr, mtart FROM mara INTO TABLE DATA(lt_mara).

Confidence:
95

Tech Stack:
SAP BTP | CAP | CDS | SQLite | SAP Business Application Studio | Fiori Preview | Cloud Foundry CLI

One key learning:
Enterprise AI solutions are built in layers:
Data Model
→ Service Layer
→ Persistence
→ User Experience
→ AI Layer
Today’s work focused on building the foundation that will support future AI-powered ABAP code review recommendations.

Next Milestone:
ABAP Code
→ AI Review Engine
→ Generated Recommendations
→ CAP Service
→ Fiori UI

#OperationSelfLearning
#SAP
#SAPBTP
#CAP
#ABAP
SAPBusinessApplicationStudio
EnterpriseArchitecture
ArtificialIntelligence
GenerativeAI
LearningInPublic

## Stats

- **Views:** 34
- **Likes:** 2
- **Shares:** 0
- **Comments:** 0

## Tags

operationselflearning, sapbtp, cap, sap, abap

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