Error Detection Mechanisms

Tired of your LLM just repeating the same mistakes when retries fail? Simple retry strategies often just multiply costs without improving reliability when models fail in consistent ways. You've built validation for structured LLM outputs, but when validation fails and you retry the exact same prompt, you're essentially asking the model to guess differently. Without feedback about what went wrong, you're wasting compute and adding latency while hoping for random success. A smarter approach feeds errors back to the model, creating a self-correcting loop. Effective AI Engineering #13: Error Reinsertion for Smarter LLM Retries 👇 The Problem ❌ Many developers implement basic retry mechanisms that blindly repeat the same prompt after a failure: [Code example - see attached image] Why this approach falls short: - Wasteful Compute: Repeatedly sending the same prompt when validation fails just multiplies costs without improving chances of success. - Same Mistakes: LLMs tend to be consistent - if they misunderstand your requirements the first time, they'll likely make the same errors on retry. - Longer Latency: Users wait through multiple failed attempts with no adaptation strategy.Beyond Blind Repetition: Making Your LLM Retries Smarter with Error Feedback. - No Learning Loop: The model never receives feedback about what went wrong, missing the opportunity to improve. The Solution: Error Reinsertion for Adaptive Retries ✅ A better approach is to reinsert error information into subsequent retry attempts, giving the model context to improve its response: [Code example - see attached image] Why this approach works better: - Adaptive Learning: The model receives feedback about specific validation failures, allowing it to correct its mistakes. - Higher Success Rate: By feeding error context back to the model, retry attempts become increasingly likely to succeed. - Resource Efficiency: Instead of hoping for random variation, each retry has a higher probability of success, reducing overall attempt count. - Improved User Experience: Faster resolution of errors means less waiting for valid responses. The Takeaway Stop treating LLM retries as mere repetition and implement error reinsertion to create a feedback loop. By telling the model exactly what went wrong, you create a self-correcting system that improves with each attempt. This approach makes your AI applications more reliable while reducing unnecessary compute and latency.

Rust's compiler catches a broad spectrum of potential bugs at compile time, ensuring they don't become runtime bugs for end users. 1. Type Errors: Mismatches between expected and actual data types, ensuring type safety. 2. Memory Safety Errors: Includes use-after-free, dangling pointers, and buffer overflows, enforced through ownership and borrowing rules. 3. Null Pointer Dereferencing: Eliminated via the `Option` type, replacing traditional null references. 4. Data Races in Concurrent Programming: Rust's ownership and borrowing model ensures safe data access in multi-threaded contexts, preventing data races. 5. Memory Leaks: Rust's ownership model typically prevents memory leaks by ensuring that all resources are freed when they go out of scope. 6. Double Free Errors: Ensures that each piece of memory is freed only once, preventing double free errors. 7. Uninitialized Variables: The compiler ensures all variables are properly initialized before use. 8. Unreachable Code: Identifies code paths that can never be executed. 9. Infinite Loops and Recursion: Detects certain cases where loops or recursive calls can lead to stack overflow. 10. Mismatched Argument Count in Function Calls: Ensures that functions are called with the correct number and type of arguments. 11. Lifetime Errors: Enforces rules about how long references are valid, preventing issues related to the lifetime of variables. 12. Immutability Violations: Prevents unauthorized modifications of data declared as immutable. 13. Exhaustiveness in Pattern Matching: Ensures that all possible cases are handled in match statements, particularly with enums. 14. Unsafe Code Misuse: Checks the interaction of 'unsafe' blocks with the safe part of the code. 15. Trait and Type Bounds Violations: Checks that generics conform to specified traits and type bounds. 16. Overflow Errors: Detects arithmetic overflows in debug mode. 17. Mismatched Types in Assignments and Return Statements: Ensures that assigned values or function returns match the declared types. 18. Incorrectly Implemented Traits: Detects when a trait is not implemented according to its required methods and signatures. 19. Invalid Reference Passing: Prevents passing references that don’t adhere to Rust’s lifetime and borrowing rules. 20. Misuse of Global Mutable State: Catches unsafe patterns in using global mutable state, which can lead to inconsistencies in concurrent contexts. 21. Block Expression Errors: Identifies issues in the last expression in a block, which in Rust is used as a return value. 22. Access to Modified Closures: Detects illegal access to closures that might have been altered during execution. 23. Incorrect Iterator Usage: Catches common mistakes in the use of iterators, such as expecting them to return a value after completion. By proactively identifying these issues, Rust’s compiler plays a crucial role in enhancing the user experience. #rustlang #rust #coding #technology

In SAP Cloud Platform Integration (CPI), custom retry mechanisms handle transient errors (e.g., network issues, HTTP 503) while avoiding retries for persistent errors (e.g., mapping issues). Below is a concise overview of retry approaches and configurations. Retry Mechanisms 1. JMS Queues   - Stores failed messages in queues for retries.   - Setup: Enable JMS (Enterprise Edition). Use JMS receiver to store messages, JMS sender to poll (e.g., every 10 mins). Set max 5 retries, exponential backoff. Move to dead-letter queue or notify after max retries.   - Use Case: Retry HTTP 503 errors.   - Pros: Reliable, visible in Queue Monitor.   - Cons: 10 GB limit, polling may pause.   - Example: Failed message retried 5 times, then emailed. 2. Data Store   - Stores messages in CPI database (32 GB) for retries.   - Setup: Passthrough iFlow to business logic iFlow. On failure, store in Data Store (“DS_RetryAllError”). Schedule retry iFlow to poll (e.g., 5 mins). Retry transient errors, move persistent to another Data Store.   - Use Case: High-volume, limited JMS.   - Pros: No JMS dependency, manual reprocessing.   - Cons: 32 GB limit, no bulk retrieval.   - Example: Connection error retried daily, mapping errors to “DS_ManualReview”. 3. HTTP Adapter Retry   - Retries HTTP errors (400–599) since CPI 7.18.   - Setup: Enable “Retry” in HTTP adapter. Set 1–3 retries, 5–60 sec intervals. Log retries.   - Use Case: Server unavailability.   - Pros: Simple, no custom logic.   - Cons: In-memory, max 3 retries.   - Example: HTTP 503 retried 3 times, fails to Exception Subprocess. 4. SuccessFactors OData V2 Retry   - Retries HTTP 429, 502, 504 errors.   - Setup: Enable “Retry on Failure”. Fixed 5 retries, 3-min intervals.   - Use Case: SuccessFactors network issues.   - Pros: Built-in.   - Cons: Fixed settings.   - Example: 502 error retried 5 times. 5. TPM Retry   - JMS-based retries for B2B since TPM 2.2.0.   - Setup: Set max retries (3–25) in Configuration Manager. Manual retry in B2B Monitor (v2.3.7).   - Use Case: B2B/EDI delivery.   - Pros: Native, manual option.   - Cons: Needs TPM 2.2.0+.   - Example: B2B message retried 3 times, manually retried. 6. XI Adapter Custom Retry   - Custom retry logic in Exception Subprocess.   - Setup: Use Local Integration Process, track retries (e.g., SAPJMSRetries). Router for retry (max 6) or alternative action.   - Use Case: Complex scenarios.   - Pros: Flexible.   - Cons: Complex setup.   - Example: Notify after 3 retries, redirect after 6.  Performance - JMS: Locks with high volumes; monitor queues. - Data Store: 32 GB limit; avoid large payloads. - HTTP Retry: Lightweight, non-persistent. - Latency: Long intervals add delays.

Schrödinger’s Cats Applied to Quantum Error Detection: Alive, Dead, and Computing A recent breakthrough has brought Schrödinger’s Cat—the famous quantum thought experiment—into the realm of quantum computing. By using a single antimony atom embedded in a silicon chip, researchers have employed features of superposition to develop a sophisticated quantum error-checking mechanism. This approach enables simultaneous detection of errors in multiple states, akin to the paradox of Schrödinger’s cat being both alive and dead. Key Insights: 1. From Thought Experiment to Reality: • Erwin Schrödinger’s concept of a cat existing in a superposition of “alive” and “dead” states was originally intended to critique quantum mechanics. • On the subatomic scale, superposition—the ability of particles to exist in two states simultaneously—is a proven phenomenon and the foundation of quantum computing. 2. Antimony Atom as a Quantum Fact-Checker: • The antimony atom embedded in silicon exhibits complex quantum behavior, surpassing the theoretical model of Schrödinger’s Cat. • This system uses superposition to detect computational errors in quantum bits (qubits), enabling precise correction mechanisms vital for quantum computing reliability. 3. Error Detection in Quantum Systems: • Traditional computers rely on binary states (1s and 0s) for calculations, while quantum computers exploit superpositions of these states for vastly more powerful computations. • Error detection and correction are critical challenges in quantum computing, as qubits are highly sensitive to environmental disturbances. Implications for Quantum Computing: This innovation demonstrates a practical application of quantum principles to improve computing reliability, addressing one of the field’s biggest hurdles: error correction. By leveraging the unique properties of superposition, this technology may pave the way for more robust quantum systems capable of handling complex calculations with minimal errors. Looking Ahead: The integration of quantum phenomena like superposition into error-detection systems highlights the transformative potential of quantum computing. Future developments could see broader adoption of such techniques, propelling advancements in fields like cryptography, artificial intelligence, and molecular simulations. This novel application of Schrödinger’s Cat metaphor underscores the growing intersection of theoretical physics and cutting-edge technology, promising a more reliable and powerful era of quantum computing.

Most asset failures are avoidable when risks are systematically identified and managed. After years of working with industrial facilities, I've found that effective risk management requires mastering five complementary frameworks: 1) HAZOP/HAZID: The foundation of process safety • HAZID provides early, broad-brush hazard identification • HAZOP deliversa systematic analysis of process deviations • Digital transformation now allows these assessments to feed directly into maintenance systems 2) FMEA (Failure Modes and Effects Analysis) • The comprehensive failure analysis framework • Now enhanced through digital twins that can simulate thousands of potential scenarios • Predictive models identify vulnerabilities that would be impossible to spot manually 3) CRA (Corrosion Risk Assessment) • Specialized analysis for material degradation mechanisms • Modern distributed sensing networks detect moisture ingress and corrosion in real-time • Early detection means addressing issues months before traditional methods would find them 4) RBI (Risk-Based Inspection) • The intelligence layer that optimizes inspection resources • AI algorithms now continuously recalculate priorities as conditions change • No more relying on outdated static schedules or calendar-based inspections 5) IOW (Integrity Operating Windows) • Defines the safe operational limits for process variables • Real-time monitoring ensures operations stay within these boundaries • Automatic alerts when parameters approach critical thresholds The power comes from integration. One refinery I worked with linked all five frameworks through a unified digital platform. Their system automatically flags when operating conditions might trigger corrosion mechanisms identified in their CRA, then updates inspection priorities in real-time. Is your organization still managing these as separate activities, or have you begun integrating them into a cohesive digital risk management strategy? *** P.S.: Looking for more in-depth industrial insights? Follow me for more on Industry 4.0, Predictive Maintenance, and the future of Corrosion Monitoring.

We should use data quality methods that can detect the most severe data quality issues. Many types of data quality issues can be detected only by one method of data quality monitoring. A data contract will not detect missing data if an error in the data pipeline prevents it from running. Data observability will not detect issues if the data distribution remains unchanged over time. Always pick a data quality validation method that can detect the most severe issues. 👉 Define data contracts if your data source changes too often. Make the data owner of the publishing platform accountable for the data format and schema. 👉 Constantly monitor data pipeline and data platform logs if you are facing reliability issues, primarily due to resource constraints - too many jobs running simultaneously. 👉 Connect a data observability platform to monitor datasets if you have a variety of datasets coming from different data sources. The data is mostly valid, but any data drift will affect data consumers like a snowball effect. 👉 Define data quality rules and test the data if your data users frequently report errors in the datasets or on dashboards. Evaluate their rules to confirm the error and detect when it reappears. I wrote this post because I received some interesting questions directly about the difference between data observability and data quality. I hope that this post, which explores other techniques, can make it easier to understand the differences. #dataquality #datagovernance #dataengineering